Curating knowledge for storage in a knowledge database

ABSTRACT

A method includes generating a plurality of entigen groups from a plurality of phrases, where the plurality of entigen groups represents a plurality of most likely meanings for the plurality of phrases. The method further includes determining an initial interpretation of the related topic based on the plurality of most likely meanings for the plurality of phrases and generating a plurality of scores for the plurality of entigen groups based on the initial interpretation and source information of the plurality of phrases. The method further includes interpreting the plurality of scores in relation to the initial interpretation to determine a confidence level of the initial interpretation and when the confidence level of the initial interpretation compares favorably to a confidence threshold, indicating that the initial interpretation is reliable.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/633,638,entitled “ANSWERING A QUESTION UTILIZING A KNOWLEDGE BASE,” filed Feb.22, 2018, which is hereby incorporated herein by reference in itsentirety and made part of the present U.S. Utility Patent Applicationfor all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computing systems and moreparticularly to generating data representations of data and analyzingthe data utilizing the data representations.

Description of Related Art

It is well known that a massive amount of data is stored in informationsystems, such as files containing text. It is often difficult to extractuseful information from this stored data. There is simply too much datafor humans to analyze. While computers may be able to process datafaster than humans, computers are challenged when it comes tocomprehending concepts or even identifying context that is often helpfulto understand the content of the data. Humans understand concepts andcontext, but cannot process the data like a computer.

It is well known that data is archived in computing systems as a moredesirable alternative to pre-computer methods such as paper-documentstorage. Regardless of the reason why the data is found in the computingsystems, the files created to communicate and store the data werecreated for human understanding, not necessarily for computerunderstanding. The information of the data can be even more useful if atruest possible meaning of the data can be identified in such a way asto enable sophisticated analysis. Computer systems struggle tounderstand the data since identifying the truest meaning has not beenpossible.

Understanding the content of data requires an ability to comprehendwords people use to convey their thoughts. If the processing power ofcomputers is to be relevant in the effort to mine massive data files,then a computerized approach must be able to comprehend text, not merelyrecognize patterns and apply statistical reasoning solutions. Thepromise of “Big Data” analytics has been difficult since the truestpossible meaning of the data cannot be easily determined from massivestorage repositories.

Relevant information is masked in an overwhelming volume of words due toambiguities inherent in words. Increasing processor speed may reduce thechallenge presented by data volume, but computer processing toproducethe truest possible meaning of the words of the data words is anunmet need. Whether simply searching data files for specific elements ofdata or extracting specific elements from data for complex analysis,current technology is limited to search-and-retrieve operations drivenby word-based pattern matching and statistical modeling. Therefore, evenwith increased computer processing power, the problem of understandingdata still exists.

It is known that one such approach to determine truest meaning is toapply sophisticated pattern matching algorithms to identify similar wordpatterns in a user's query with those in the data. These algorithms havebecome quite pervasive, but the basic approach remains that ofprobabilistic scoring of queries and data indexes to establish the bestmatch between query and data—i.e., statistical reasoning solutions. If aspecific answer resides in the database, such as “George Washington wasthe first President of the United States”, then current software canreturn that answer in response to the query, “Who was the firstPresident of the United States?” The answer was explicit in thedatabase. If the answer is not explicit in the data, then the matchingalgorithm will return a list of possible files where the answer might befound. It is then incumbent on a requester to read through the list ofpotentially relevant documents and find the answer implicit in the data.

A good example of the problem that such a lack of meaning understandingcreates is demonstratedin the following; “Who died today?” In thissimple three-word query, the requester wants to know the names of thepeople who died on this day. But a computer is challenged to comprehendthis simple question. Instead, the computer algorithm focuses on the keywords “died” and “today”. Most algorithms are sufficiently sophisticatedto deal with words similar to “died” or its general meaning, such as“death”, “killed”, “passed away”, etc. But more challenging is dealingwith the temporal nature of the word “today.” Consequently, if one wereto do an internet search with a “search engine”, one would get aboutover 500,000 responses or hits—alist of links to over 500,000 uniformresource locators (URLs) to sift through. Even if a requester couldopen, read, and extract useful information from these URLs at the rateof one per second (which is not likely), it would take 150 person-hours(3.75 standard work weeks at 40-hours/week) to get through the entirelist. But worse than that, because the computer algorithm did notunderstand the question, none of the results answer the question.Instead, the word-pattern matching returns a list of URLs that link todeaths: reported in media records with the word “today” in the title(e.g., “New York Today News”), that occurred today in history, or thatwere reported today, etc. When answering a query requires understandingboth the query and the text files, word-based pattern matching isinadequate.

It is further well known that answering queries with current technologyrelies on the answer being explicit in the text. In order to increasethe likelihood that an answer to a potential query will be explicit inthe data, one known approach is to seed the data with answers to likelyquestions. For example, a super computer downloads large data base filesand then sets an army of human experts to work reading though the filesand preparing a list of question/answer pairs which are loaded into thesuper computer. When asked a question by a requester, the super computerstatistically searches for the highest correlation between the wordpattern in the query and the word pattern in the pre-loaded answers.This approach is known as statistical reasoning. Not only is thisprocess of generating question and answer pairs exceedingly expensiveand time consuming, it also limits the information that can be extractedfrom the data to those questions that have been previously created andstored. What is advertised to a layperson as “thinking”, but it isactually no more than retrieving preselected answers from large datafiles. While such an approach may be adequate for static data sources,such as voluminous government regulations, historical fact tables, ormedical diagnostic decision trees, it cannot provide insights intodynamic data (e.g., real time), nor can it leverage the inexhaustiblepower of a computer's ability to find all relevant, co-related data inhuge data files.

It is also well known that many industry leaders are attempting to usedeep neural networks to identify objects in photos and recognize theindividual words we speak into digital assistants (e.g., consumer voicerecognition computer assistance systems). The hope is that this type ofartificial intelligence can dramatically improve a machine's ability tograsp the significance of those words by understanding how those wordsinteract to form meaningful sentences. These industry leaders haverecognized the importance of comprehending words as an enabler for awide range of computer functions. But, the neural-net approach stillrelies on pattern matching and probabilistic scoring and requires somelevel of supervised learning.

Currently, available technology simply cannot provide insightfulknowledge. It can only provide a list of potentially relevant sources toserve as leads for humans to process manually who then generateinsightful knowledge, or it can search a database of pre-loadedknowledge generated by experts. But it cannot provide insightfulknowledge, or extract all the relevant data for additional analysis fromdata files because it cannot comprehend the meaning(s) in data, such astext.

As an interesting comparison, humans can read and comprehend text files,but cannot process data fast enough to sort through massive filesquickly. Computers can process data quickly, but cannot comprehend theconcepts and context conveyed by text as humans do. Either humans needto process at computer speeds or computers need to comprehend at humanlevels. The technology required to achieve the former is not on thehorizon. The technology to achieve the latter has not yet beendemonstrated by known approaches.

Therefore, there is useful information that resides in massive datastored in information systems that cannot currently be understood bycomputers for analysis. The promise of “Big Data” analytics cannot bemet if the relevant data cannot be understood and extracted from itsmassive storage repositories. This current lack of understandingeffectively buries relevant information in overwhelming volume and masksit with the ambiguities inherent in words. Increasing processor speedwill reduce the challenge presented by data volume, but no currentlyavailable software will overcome the challenge presented by words.

Currently, the generalized approach employed by all the technologies andmethodologies mentioned above is that the fundamental understanding ofnatural languages is based on grammar. Specifically, grammar classifieswords into five major grammatical types such as functional words, nouns,adjectives, verbs and adverbs. Grammar then uses these grammatical typesto study how words should be distributed within a string of words toform a properly constructed grammatical sentence. However, understandingnatural languages from a grammatical stance encourages the desertion oftwo major and crucial points. The first point is that grammar does notreflect the mind's natural ability to learn, create and achieve languageand speech. People from all ages and cultures can communicate usingnatural languages without any formal grammatical training or expertise.The second point is that grammar is not concerned with the words'descriptive purpose or with the things that words are actually trying todescribe or identify, but rather with the words' own grammaticaloperations and purpose (how the word is used within sentences).

This later point forces grammar to divide words that describe singleideas into separate grammatical identifications. For example,grammatically speaking, the word “human” is divided into a noun and anadjective based on how the word “human” is being used within a givensentence. In the sentence “the feelings of a human are profound” theword “human” is a noun, because “human” operates as a noun. But in thesentence “human feelings are profound” the word “human” is now anadjective, because grammatically speaking “human” in this example isoperating as an adjective. Another example is what happens to the word“talking”, which grammatically speaking is divided into three differentelements such as a noun, an adjective or a verb. In “the talking of thepresident” the word “talking” is a noun; in “the talking president leftthe building” the word “talking” is an adjective; and in “the presidentis talking” the word “talking” is a verb. As a result, grammar not onlydivided “human” in two diverging terms and “talking” into threecompletely different terms, but in addition, grammar added complexity,because the grammatical classifications of “human” and “talking” werebased on usage of each word within each sentence, not meaning.

But more importantly, grammar ignored the most important aspect behindeach word, and that is what every word (human and talking) is actuallytrying to describe. Descriptively speaking (not grammatically speaking),the word “human” always describes a living being and the word “talking”always describes the same type of action on all sentences above. Anotherserious limitation that grammar endows is that most colloquial andconversational communications between people are informal and thereforedo not follow the rigidness or sophistication demanded by the rules ofgrammar. This limits grammatical-based technologies from processing thistype of data. Consequentially, grammar, aside from adding complexity andunnecessary partitioning of words, also reduces the capacity ofcomputers and software to be flexible to process and understand whatpeople are naturally saying, writing or implying. In view of theforegoing, there is an ongoing need for providing systems and methods ofidentifying words differently for processing natural languages includingcreating and maintaining searchable databases that when queried by auser produce results that are precisely and accurately responsive to theuser's query. Moreover, because the purpose of ontological categories isto distinguish the elements it studies, failing to properlydifferentiate such elements from each other leads to seriousinconsistencies. Indeed, if we select the wrong parameters todistinguish and study the elements of a given system, the resultingcategories could be obtrusive and contradictory. For example, if“motion” is selected as the parameter to study the pieces of a train;then the engine, the passenger cars and the caboose can be confused,because all these pieces experience the same type of motion when thetrain moves. This is similar to what grammar has done by defining howwords are used instead of what words describe. Stating how a word isused in a sentence does not identify what the word is actually trying todescribe. This has led grammatical approaches to create obtrusivecategories, confusion and contradictions in semantic and meaning-basedanalysis. For example, in many dictionaries the word “elected” isdivided as an adjective and a verb (this is obtrusive within thatdictionary); while in other dictionaries, “elected” is only an adjectiveor only a verb (this is contradictory among dictionaries).

In addition, current technology is focused on identifying the part ofspeech a word represents as opposed to what the word is describing orintending to describe. To date, this approach or methodology precludescurrent technology from recognizing the single unique individuals,unique items, or unique things that words represent or are trying todescribe in time and space. Nor can current technology assign orassociate actions and/or attributes to a unique single individual, itemor thing and vice versa. Therefore, there is need in the art for methodsand approaches that can analysis big data and address the limitationsidentified above.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of various serversof a computing system in accordance with the present invention;

FIG. 3 is a schematic block diagram of an embodiment of various devicesof a computing system in accordance with the present invention;

FIGS. 4A and 4B are schematic block diagrams of another embodiment of acomputing system in accordance with the present invention;

FIG. 4C is a logic diagram of an embodiment of a method for interpretingcontent to produce a response to a query within a computing system inaccordance with the present invention;

FIG. 5A is a schematic block diagram of an embodiment of a collectionsmodule of a computing system in accordance with the present invention;

FIG. 5B is a logic diagram of an embodiment of a method for obtainingcontent within a computing system in accordance with the presentinvention;

FIG. 5C is a schematic block diagram of an embodiment of a query moduleof a computing system in accordance with the present invention;

FIG. 5D is a logic diagram of an embodiment of a method for providing aresponse to a query within a computing system in accordance with thepresent invention;

FIG. 5E is a schematic block diagram of an embodiment of an identigenentigen intelligence (WI) module of a computing system in accordancewith the present invention;

FIG. 5F is a logic diagram of an embodiment of a method for analyzingcontent within a computing system in accordance with the presentinvention;

FIG. 6A is a schematic block diagram of an embodiment of an elementidentification module and an interpretation module of a computing systemin accordance with the present invention;

FIG. 6B is a logic diagram of an embodiment of a method for interpretinginformation within a computing system in accordance with the presentinvention;

FIG. 6C is a schematic block diagram of an embodiment of an answerresolution module of a computing system in accordance with the presentinvention;

FIG. 6D is a logic diagram of an embodiment of a method for producing ananswer within a computing system in accordance with the presentinvention;

FIG. 7A is an information flow diagram for interpreting informationwithin a computing system in accordance with the present invention;

FIG. 7B is a relationship block diagram illustrating an embodiment ofrelationships between things and representations of things within acomputing system in accordance with the present invention;

FIG. 7C is a diagram of an embodiment of a synonym words table within acomputing system in accordance with the present invention;

FIG. 7D is a diagram of an embodiment of a polysemous words table withina computing system in accordance with the present invention;

FIG. 7E is a diagram of an embodiment of transforming words intogroupings within a computing system in accordance with the presentinvention;

FIG. 8A is a data flow diagram for accumulating knowledge within acomputing system in accordance with the present invention;

FIG. 8B is a diagram of an embodiment of a groupings table within acomputing system in accordance with the present invention;

FIG. 8C is a data flow diagram for answering questions utilizingaccumulated knowledge within a computing system in accordance with thepresent invention;

FIG. 8D is a data flow diagram for answering questions utilizinginterference within a computing system in accordance with the presentinvention;

FIG. 8E is a relationship block diagram illustrating another embodimentof relationships between things and representations of things within acomputing system in accordance with the present invention;

FIGS. 8F and 8G are schematic block diagrams of another embodiment of acomputing system in accordance with the present invention;

FIG. 8H is a logic diagram of an embodiment of a method for processingcontent to produce knowledge within a computing system in accordancewith the present invention;

FIGS. 8J and 8K are schematic block diagrams another embodiment of acomputing system in accordance with the present invention;

FIG. 8L is a logic diagram of an embodiment of a method for generating aquery response to a query within a computing system in accordance withthe present invention;

FIG. 9A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 9B is a data flow diagram for answering questions utilizingaccumulated knowledge within a computing system in accordance with thepresent invention;

FIG. 9C is a logic diagram of an embodiment of a method for producing aresponse to a query within a computing system in accordance with thepresent invention;

FIG. 10A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 10B is a data flow diagram for predicting an attack utilizingpre-attack sequence detection within a computing system in accordancewith the present invention;

FIG. 10C is a logic diagram of an embodiment of a method for predictingan attack within a computing system in accordance with the presentinvention;

FIG. 11A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 11B is a data flow diagram for providing an answer to a questionwithin a computing system in accordance with the present invention;

FIG. 11C is a logic diagram of an embodiment of a method for providingan answer to a question within a computing system in accordance with thepresent invention;

FIG. 12A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 12B is a data flow diagram for providing an answer to a questionwith regards to factual likelihood within a computing system inaccordance with the present invention;

FIGS. 12C-D are data flow diagrams for curating knowledge within acomputing system in accordance with the present invention;

FIG. 12E is a logic diagram of an embodiment of a method for curatingknowledge within a computing system in accordance with the presentinvention;

FIG. 13A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIGS. 13B-13D are process flow diagrams of an embodiment of a method totranslate words of a first language into words of a second language inaccordance with the present invention;

FIG. 13E is a logic diagram of an embodiment of a method for translatingwords of a first language into words of a second language within acomputing system in accordance with the present invention;

FIG. 14A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 14B is a logic diagram of an embodiment of a method for providingan answer to a question with regards to improved route guidance within acomputing system in accordance with the present invention;

FIG. 15A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 15B is a data flow diagram for generating a predicted action alertwithin a computing system in accordance with the present invention;

FIG. 15C is a logic diagram of an embodiment of a method for generatinga predicted action alert within a computing system in accordance withthe present invention;

FIG. 16A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 16B is a data flow diagram for identifying an author within acomputing system in accordance with the present invention; and

FIG. 16C is a logic diagram of an embodiment of a method for identifyingan author within a computing system in accordance with the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem 10 that includes a plurality of user devices 12-1 through 12-N, aplurality of wireless user devices 14-1 through 14-N, a plurality ofcontent sources 16-1 through 16-N, a plurality of transactional servers18-1 through 18-N, a plurality of artificial intelligence (AI) servers20-1 through 20-N, and a core network 24. The core network 24 includesat least one of the Internet, a public radio access network (RAN), andany private network. Hereafter, the computing system 10 may beinterchangeably referred to as a data network, a data communicationnetwork, a system, a communication system, and a data communicationsystem. Hereafter, the user device and the wireless user device may beinterchangeably referred to as user devices, and each of thetransactional servers and the AI servers may be interchangeably referredto as servers.

Each user device, wireless user device, transactional server, and AIserver includes a computing device that includes a computing core. Ingeneral, a computing device is any electronic device that cancommunicate data, process data, and/or store data. A further generalityof a computing device is that it includes one or more of a centralprocessing unit (CPU), a memory system, a sensor (e.g., internal orexternal), user input/output interfaces, peripheral device interfaces,communication elements, and an interconnecting bus structure.

As further specific examples, each of the computing devices may be aportable computing device and/or a fixed computing device. A portablecomputing device may be an embedded controller, a smart sensor, a smartpill, a social networking device, a gaming device, a cell phone, a smartphone, a robot, a personal digital assistant, a digital music player, adigital video player, a laptop computer, a handheld computer, a tablet,a video game controller, an engine controller, a vehicular controller,an aircraft controller, a maritime vessel controller, a spacecraftcontroller, and/or any other portable device that includes a computingcore. A fixed computing device may be security camera, a sensor device,a household appliance, a machine, a robot, an embedded controller, apersonal computer (PC), a computer server, a cable set-top box, asatellite receiver, a television set, a printer, a fax machine, homeentertainment equipment, a camera controller, a video game console, acritical infrastructure controller, and/or any type of home or officecomputing equipment that includes a computing core. An embodiment of thevarious servers is discussed in greater detail with reference to FIG. 2.An embodiment of the various devices is discussed in greater detail withreference to FIG. 3.

Each of the content sources 16-1 through 16-N includes any source ofcontent, where the content includes one or more of data files, a datastream, a tech stream, a text file, an audio stream, an audio file, avideo stream, a video file, etc. Examples of the content sources includea weather service, a multi-language online dictionary, a fact server, abig data storage system, the Internet, social media systems, an emailserver, a news server, a schedule server, a traffic monitor, a securitycamera system, audio monitoring equipment, an information server, aservice provider, a data aggregator, and airline traffic server, ashipping and logistics server, a banking server, a financial transactionserver, etc. Alternatively, or in addition to, one or more of thevarious user devices may provide content. For example, a wireless userdevice may provide content (e.g., issued as a content message) when thewireless user device is able to capture data (e.g., text input, sensorinput, etc.).

Generally, an embodiment of this invention presents solutions where thecomputing system 10 supports the generation and utilization of knowledgeextracted from content. For example, the AI servers 20-1 through 20-Ningest content from the content sources 16-1 through 16-N by receiving,via the core network 24 content messages 28-1 through 28-N as AImessages 32-1 through 32-N, extract the knowledge from the ingestedcontent, and interact with the various user devices to utilize theextracted knowledge by facilitating the issuing, via the core network24, user messages 22-1 through 22-N to the user devices 12-1 through12-N and wireless signals 26-1 through 26-N to the wireless user devices14-1 through 14-N.

Each content message 28-1 through 28-N includes a content request (e.g.,requesting content related to a topic, content type, content timing, oneor more domains, etc.) or a content response, where the content responseincludes real-time or static content such as one or more of dictionaryinformation, facts, non-facts, weather information, sensor data, newsinformation, blog information, social media content, user daily activityschedules, traffic conditions, community event schedules, schoolschedules, user schedules airline records, shipping records, logisticsrecords, banking records, census information, global financial historyinformation, etc. Each AI message 32-1 through 32-N includes one or moreof content messages, user messages (e.g., a query request, a queryresponse that includes an answer to a query request), and transactionmessages (e.g., transaction information, requests and responses relatedto transactions). Each user message 22-1 through 22-N includes one ormore of a query request, a query response, a trigger request, a triggerresponse, a content collection, control information, softwareinformation, configuration information, security information, routinginformation, addressing information, presence information, analyticsinformation, protocol information, all types of media, sensor data,statistical data, user data, error messages, etc.

When utilizing a wireless signal capability of the core network 24, eachof the wireless user devices 14-1 through 14-N encodes/decodes dataand/or information messages (e.g., user messages such as user messages22-1 through 22-N) in accordance with one or more wireless standards forlocal wireless data signals (e.g., Wi-Fi, Bluetooth, ZigBee) and/or forwide area wireless data signals (e.g., 2G, 3G, 4G, 5G, satellite,point-to-point, etc.) to produce wireless signals 26-1 through 26-N.Having encoded/decoded the data and/or information messages, thewireless user devices 14-1 through 14-N and/receive the wireless signalsto/from the wireless capability of the core network 24.

As another example of the generation and utilization of knowledge, thetransactional servers 18-1 through 18-N communicate, via the corenetwork 24, transaction messages 30-1 through 30-N as further AImessages 32-1 through 32-N to facilitate ingesting of transactional typecontent (e.g., real-time crypto currency transaction information) and tofacilitate handling of utilization of the knowledge by one or more ofthe transactional servers (e.g., for a transactional function) inaddition to the utilization of the knowledge by the various userdevices. Each transaction message 30-1 through 30-N includes one or moreof a query request, a query response, a trigger request, a triggerresponse, a content message, and transactional information, where thetransactional information may include one or more of consumer purchasinghistory, crypto currency ledgers, stock market trade information, otherinvestment transaction information, etc.

In another specific example of operation of the generation andutilization of knowledge extracted from the content, the user device12-1 issues a user message 22-1 to the AI server 20-1, where the usermessage 22-1 includes a query request and where the query requestincludes a question related to a first domain of knowledge. The issuingincludes generating the user message 22-1 based on the query request(e.g., the question), selecting the AI server 20-1 based on the firstdomain of knowledge, and sending, via the core network 24, the usermessage 22-1 as a further AI message 32-1 to the AI server 20-1. Havingreceived the AI message 32-1, the AI server 20-1 analyzes the questionwithin the first domain, generates further knowledge, generates apreliminary answer, generates a quality level indicator of thepreliminary answer, and determines to gather further content when thequality level indicator is below a minimum quality threshold level.

When gathering the further content, the AI server 20-1 issues, via thecore network 24, a still further AI message 32-1 as a further contentmessage 28-1 to the content source 16-1, where the content message 28-1includes a content request for more content associated with the firstdomain of knowledge and in particular the question. Alternatively, or inaddition to, the AI server 20-1 issues the content request to another AIserver to facilitate a response within a domain associated with theother AI server. Further alternatively, or in addition to, the AI server20-1 issues the content request to one or more of the various userdevices to facilitate a response from a subject matter expert. Havingreceived the content message 28-1, the contents or 16-1 issues, via thecore network 24, a still further content message 28-1 to the AI server20-1 as a yet further AI message 32-1, where the still further contentmessage 28-1 includes requested content. The AI server 20-1 processesthe received content to generate further knowledge. Having generated thefurther knowledge, the AI server 20-1 re-analyzes the question,generates still further knowledge, generates another preliminary answer,generates another quality level indicator of the other preliminaryanswer, and determines to issue a query response to the user device 12-1when the quality level indicator is above the minimum quality thresholdlevel. When issuing the query response, the AI server 20-1 generates anAI message 32-1 that includes another user message 22-1, where the otheruser message 22-1 includes the other preliminary answer as a queryresponse including the answer to the question. Having generated the AImessage 32-1, the AI server 20-1 sends, via the core network 24, the AImessage 32-1 as the user message 22-1 to the user device 12-1 thusproviding the answer to the original question of the query request.

FIG. 2 is a schematic block diagram of an embodiment of the AI servers20-1 through 20-N and the transactional servers 18-1 through 18-N of thecomputing system 10 of FIG. 1. The servers may include a computing core52, one or more visual output devices 74 (e.g., video graphics display,touchscreen, LED, etc.), one or more user input devices 76 (e.g.,keypad, keyboard, touchscreen, voice to text, a push button, amicrophone, a card reader, a door position switch, a biometric inputdevice, etc.), one or more audio output devices 78 (e.g., speaker(s),headphone jack, a motor, etc.), one or more visual input devices 80(e.g., a still image camera, a video camera, photocell, etc.), one ormore universal serial bus (USB) devices (USB devices 1-U), one or moreperipheral devices (e.g., peripheral devices 1-P), one or more memorydevices (e.g., one or more flash memory devices 92, one or more harddrive (HD) memories 94, one or more solid state (SS) memory devices 96,and/or cloud memory 98), one or more wireless location modems 84 (e.g.,global positioning satellite (GPS), Wi-Fi, angle of arrival, timedifference of arrival, signal strength, dedicated wireless location,etc.), one or more wireless communication modems 86-1 through 86-N(e.g., a cellular network transceiver, a wireless data networktransceiver, a Wi-Fi transceiver, a Bluetooth transceiver, a 315 MHztransceiver, a zig bee transceiver, a 60 GHz transceiver, etc.), a telcointerface 102 (e.g., to interface to a public switched telephonenetwork), a wired local area network (LAN) 88 (e.g., optical,electrical), a wired wide area network (WAN) 90 (e.g., optical,electrical), and an energy source 100 (e.g., a battery, a solar powersource, a fuel cell, a capacitor, a generator, mains power, backuppower, etc.).

The computing core 52 includes a video graphics module 54, one or moreprocessing modules 50-1 through 50-N (e.g., which may include one ormore secure co-processors), a memory controller 56, one or more mainmemories 58-1 through 58-N (e.g., RAM), one or more input/output (I/O)device interfaces 62, an input/output (I/O) controller 60, a peripheralinterface 64, one or more USB interfaces 66, one or more networkinterfaces 72, one or more memory interfaces 70, and/or one or moreperipheral device interfaces 68.

The processing modules may be a single processing device or a pluralityof processing devices where the processing device may further bereferred to as one or more of a “processing circuit”, a “processor”,and/or a “processing unit”. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module, module, processingcircuit, and/or processing unit may be, or further include, memoryand/or an integrated memory element, which may be a single memorydevice, a plurality of memory devices, and/or embedded circuitry ofanother processing module, module, processing circuit, and/or processingunit. Such a memory device may be a read-only memory, random accessmemory, volatile memory, non-volatile memory, static memory, dynamicmemory, flash memory, cache memory, and/or any device that storesdigital information. Note that if the processing module, module,processing circuit, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

Each of the interfaces 62, 66, 68, 70, and 72 includes a combination ofhardware (e.g., connectors, wiring, etc.) and may further includeoperational instructions stored on memory (e.g., driver software) thatare executed by one or more of the processing modules 50-1 through 50-Nand/or a processing circuit within the interface. Each of the interfacescouples to one or more components of the servers. For example, one ofthe IO device interfaces 62 couples to an audio output device 78. Asanother example, one of the memory interfaces 70 couples to flash memory92 and another one of the memory interfaces 70 couples to cloud memory98 (e.g., an on-line storage system and/or on-line backup system). Inother embodiments, the servers may include more or less devices andmodules than shown in this example embodiment of the servers.

FIG. 3 is a schematic block diagram of an embodiment of the variousdevices of the computing system 10 of FIG. 1, including the user devices12-1 through 12-N and the wireless user devices 14-1 through 14-N. Thevarious devices include the visual output device 74 of FIG. 2, the userinput device 76 of FIG. 2, the audio output device 78 of FIG. 2, thevisual input device 80 of FIG. 2, and one or more sensors 82 implementedinternally and/or externally to the device (e.g., a still camera, avideo camera, servo motors associated with a camera, a positiondetector, a smoke detector, a gas detector, a motion sensor, anaccelerometer, velocity detector, a compass, a gyro, a temperaturesensor, a pressure sensor, an altitude sensor, a humidity detector, amoisture detector, an imaging sensor, a biometric sensor, an infraredsensor, an audio sensor, an ultrasonic sensor, a proximity detector, amagnetic field detector, a biomaterial detector, a radiation detector, aweight detector, a density detector, a chemical analysis detector, afluid flow volume sensor, a DNA reader, a wind speed sensor, a winddirection sensor, an object detection sensor, an object identifiersensor, a motion recognition detector, a battery level detector, a roomtemperature sensor, a sound detector, a smoke detector, an intrusiondetector, a motion detector, a door position sensor, a window positionsensor, a sunlight detector, and medical category sensors including: apulse rate monitor, a heart rhythm monitor, a breathing detector, ablood pressure monitor, a blood glucose level detector, blood type, anelectrocardiogram sensor, a body mass detector, an imaging sensor, amicrophone, body temperature, etc.).

The various devices further include the computing core 52 of FIG. 2, theone or more universal serial bus (USB) devices (USB devices 1-U) of FIG.2, the one or more peripheral devices (e.g., peripheral devices 1-P) ofFIG. 2, the one or more memories of FIG. 2 (e.g., flash memories 92, HDmemories 94, SS memories 96, and/or cloud memories 98), the one or morewireless location modems 84 of FIG. 2, the one or more wirelesscommunication modems 86-1 through 86-N of FIG. 2, the telco interface102 of FIG. 2, the wired local area network (LAN) 88 of FIG. 2, thewired wide area network (WAN) 90 of FIG. 2, and the energy source 100 ofFIG. 2. In other embodiments, the various devices may include more orless internal devices and modules than shown in this example embodimentof the various devices.

FIGS. 4A and 4B are schematic block diagrams of another embodiment of acomputing system that includes one or more of the user device 12-1 ofFIG. 1, the wireless user device 14-1 of FIG. 1, the content source 16-1of FIG. 1, the transactional server 18-1 of FIG. 1, the user device 12-2of FIG. 1, and the AI server 20-1 of FIG. 1. The AI server 20-1 includesthe processing module 50-1 (e.g., associated with the servers) of FIG.2, where the processing module 50-1 includes a collections module 120,an identigen entigen intelligence (IEI) module 122, and a query module124. Alternatively, the collections module 120, the IEI module 122, andthe query module 124 may be implemented by the processing module 50-1(e.g., associated with the various user devices) of FIG. 3. Thecomputing system functions to interpret content to produce a response toa query.

FIG. 4A illustrates an example of the interpreting of the content toproduce the response to the query where the collections module 120interprets (e.g., based on an interpretation approach such as rules) atleast one of a collections request 132 from the query module 124 and acollections request within collections information 130 from the IEImodule 122 to produce content request information (e.g., potentialsources, content descriptors of desired content). Alternatively, or inaddition to, the collections module 120 may facilitate gathering furthercontent based on a plurality of collection requests from a plurality ofdevices of the computing system 10 of FIG. 1.

The collections request 132 is utilized to facilitate collection ofcontent, where the content may be received in a real-time fashion onceor at desired intervals, or in a static fashion from previous discretetime frames. For instance, the query module 124 issues the collectionsrequest 132 to facilitate collection of content as a background activityto support a long-term query (e.g., how many domestic airline flightsover the next seven days include travelers between the age of 18 and 35years old). The collections request 132 may include one or more of arequester identifier (ID), a content type (e.g., language, dialect,media type, topic, etc.), a content source indicator, securitycredentials (e.g., an authorization level, a password, a user ID,parameters utilized for encryption, etc.), a desired content qualitylevel, trigger information (e.g., parameters under which to collectcontent based on a pre-event, an event (i.e., content quality levelreaches a threshold to cause the trigger, trueness), or a timeframe), adesired format, and a desired timing associated with the content.

Having interpreted the collections request 132, the collections module120 selects a source of content based on the content requestinformation. The selecting includes one or more of identifying one ormore potential sources based on the content request information,selecting the source of content from the potential sources utilizing aselection approach (e.g., favorable history, a favorable security level,favorable accessibility, favorable cost, favorable performance, etc.).For example, the collections module 120 selects the content source 16-1when the content source 16-1 is known to provide a favorable contentquality level for a domain associated with the collections request 132.

Having selected the source of content, the collections module 120 issuesa content request 126 to the selected source of content. The issuingincludes generating the content request 126 based on the content requestinformation for the selected source of content and sending the contentrequest 126 to the selected source of content. The content request 126may include one or more of a content type indicator, a requester ID,security credentials for content access, and any other informationassociated with the collections request 132. For example, thecollections module 120 sends the content request 126, via the corenetwork 24 of FIG. 1, to the content source 16-1. Alternatively, or inaddition to, the collections module 120 may send a similar contentrequest 126 to one or more of the user device 12-1, the wireless userdevice 14-1, and the transactional server 18-1 to facilitate collectingof further content.

In response to the content request 126, the collections module 120receives one or more content responses 128. The content response 128includes one or more of content associated with the content source, acontent source identifier, security credential processing information,and any other information pertaining to the desired content. Havingreceived the content response 128, the collections module 120 interpretsthe received content response 128 to produce collections information130, where the collections information 130 further includes acollections response from the collections module 120 to the IEI module122. The collections response includes one or more of transformedcontent (e.g., completed sentences and paragraphs), timing informationassociated with the content, a content source ID, and a content qualitylevel. Having generated the collections response of the collectionsinformation 130, the collections module 120 sends the collectionsinformation 130 to the IEI module 122. Having received the collectionsinformation 130 from the collections module 120, the IEI module 122interprets the further content of the content response to generatefurther knowledge, where the further knowledge is stored in a memoryassociated with the IEI module 122 to facilitate subsequent answering ofquestions posed in received queries.

FIG. 4B further illustrates the example of the interpreting of thecontent to produce the response to the query where, the query module 124interprets a received query request 136 from a requester to produce aninterpretation of the query request. For example, the query module 124receives the query request 136 from the user device 12-2, and/or fromone or more of the wireless user device 14-2 and the transactionalserver 18-2. The query request 136 includes one or more of an identifier(ID) associated with the request (e.g., requester ID, ID of an entity tosend a response to), a question, question constraints (e.g., within atimeframe, within a geographic area, within a domain of knowledge,etc.), and content associated with the question (e.g., which may beanalyzed for new knowledge itself).

The interpreting of the query request 136 includes determining whetherto issue a request to the IEI module 122 (e.g., a question, perhaps withcontent) and/or to issue a request to the collections module 120 (e.g.,for further background content). For example, the query module 124produces the interpretation of the query request to indicate to send therequest directly to the IEI module 122 when the question is associatedwith a simple non-time varying function answer (e.g., question: “howmany hydrogen atoms does a molecule of water have?”).

Having interpreted the query request 136, the query module 124 issues atleast one of an IEI request as query information 138 to the IEI module122 (e.g., when receiving a simple new query request) and a collectionsrequest 132 to the collections module 120 (e.g., based on two or morequery requests 136 requiring more substantive content gathering). TheIEI request of the query information 138 includes one or more of anidentifier (ID) of the query module 124, an ID of the requester (e.g.,the user device 12-2), a question (e.g., with regards to content foranalysis, with regards to knowledge minded by the AI server from generalcontent), one or more constraints (e.g., assumptions, restrictions,etc.) associated with the question, content for analysis of thequestion, and timing information (e.g., a date range for relevance ofthe question).

Having received the query information 138 that includes the IEI requestfrom the query module 124, the IEI module 122 determines whether asatisfactory response can be generated based on currently availableknowledge, including that of the query request 136. The determiningincludes indicating that the satisfactory response cannot be generatedwhen an estimated quality level of an answer falls below a minimumquality threshold level. When the satisfactory response cannot begenerated, the IEI module 122 facilitates collecting more content. Thefacilitating includes issuing a collections request to the collectionsmodule 120 of the AI server 20-1 and/or to another server or userdevice, and interpreting a subsequent collections response 134 ofcollections information 130 that includes further content to producefurther knowledge to enable a more favorable answer.

When the IEI module 122 indicates that the satisfactory response can begenerated, the IEI module 122 issues an IEI response as queryinformation 138 to the query module 124. The IEI response includes oneor more of one or more answers, timing relevance of the one or moreanswers, an estimated quality level of each answer, and one or moreassumptions associated with the answer. The issuing includes generatingthe IEI response based on the collections response 134 of thecollections information 130 and the IEI request, and sending the IEIresponse as the query information 138 to the query module 124.Alternatively, or in addition to, at least some of the further contentcollected by the collections module 120 is utilized to generate acollections response 134 issued by the collections module 120 to thequery module 124. The collections response 134 includes one or more offurther content, a content availability indicator (e.g., when, where,required credentials, etc.), a content freshness indicator (e.g.,timestamps, predicted time availability), content source identifiers,and a content quality level.

Having received the query information 138 from the IEI module 122, thequery module 124 issues a query response 140 to the requester based onthe IEI response and/or the collections response 134 directly from thecollections module 120, where the collection module 120 generates thecollections response 134 based on collected content and the collectionsrequest 132. The query response 140 includes one or more of an answer,answer timing, an answer quality level, and answer assumptions.

FIG. 4C is a logic diagram of an embodiment of a method for interpretingcontent to produce a response to a query within a computing system. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-3,4A-4B, and also FIG. 4C. The method includes step 150 where acollections module of a processing module of one or more computingdevices (e.g., of one or more servers) interprets a collections requestto produce content request information. The interpreting may include oneor more of identifying a desired content source, identifying a contenttype, identifying a content domain, and identifying content timingrequirements.

The method continues at step 152 where the collections module selects asource of content based on the content request information. For example,the collections module identifies one or more potential sources based onthe content request information and selects the source of content fromthe potential sources utilizing a selection approach (e.g., based on oneor more of favorable history, a favorable security level, favorableaccessibility, favorable cost, favorable performance, etc.). The methodcontinues at step 154 where the collections module issues a contentrequest to the selected source of content. The issuing includesgenerating a content request based on the content request informationfor the selected source of content and sending the content request tothe selected source of content.

The method continues at step 156 where the collections module issuescollections information to an identigen entigen intelligence (IEI)module based on a received content response, where the IEI moduleextracts further knowledge from newly obtained content from the one ormore received content responses. For example, the collections modulegenerates the collections information based on newly obtained contentfrom the one or more received content responses of the selected sourceof content.

The method continues at step 158 where a query module interprets areceived query request from a requester to produce an interpretation ofthe query request. The interpreting may include determining whether toissue a request to the IEI module (e.g., a question) or to issue arequest to the collections module to gather further background content.The method continues at step 160 where the query module issues a furthercollections request. For example, when receiving a new query request,the query module generates a request for the IEI module. As anotherexample, when receiving a plurality of query requests for similarquestions, the query module generates a request for the collectionsmodule to gather further background content.

The method continues at step 162 where the IEI module determines whethera satisfactory query response can be generated when receiving therequest from the query module. For example, the IEI module indicatesthat the satisfactory query response cannot be generated when anestimated quality level of an answer is below a minimum answer qualitythreshold level. The method branches to step 166 when the IEI moduledetermines that the satisfactory query response can be generated. Themethod continues to step 164 when the IEI module determines that thesatisfactory query response cannot be generated. When the satisfactoryquery response cannot be generated, the method continues at step 164where the IEI module facilitates collecting more content. The methodloops back to step 150.

When the satisfactory query response can be generated, the methodcontinues at step 166 where the IEI module issues an IEI response to thequery module. The issuing includes generating the IEI response based onthe collections response and the IEI request, and sending the IEIresponse to the query module. The method continues at step 168 where thequery module issues a query response to the requester. For example, thequery module generates the query response based on the IEI responseand/or a collections response from the collections module and sends thequery response to the requester, where the collections module generatesthe collections response based on collected content and the collectionsrequest.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 5A is a schematic block diagram of an embodiment of the collectionsmodule 120 of FIG. 4A that includes a content acquisition module 180, acontent selection module 182, a source selection module 184, a contentsecurity module 186, an acquisition timing module 188, a contenttransformation module 190, and a content quality module 192. Generally,an embodiment of this invention presents solutions where the collectionsmodule 120 supports collecting content.

In an example of operation of the collecting of the content, the contentacquisition module 180 receives a collections request 132 from arequester. The content acquisition module 180 obtains content selectioninformation 194 based on the collections request 132. The contentselection information 194 includes one or more of content requirements,a desired content type indicator, a desired content source identifier, acontent type indicator, a candidate source identifier (ID), and acontent profile (e.g., a template of typical parameters of the content).For example, the content acquisition module 180 receives the contentselection information 194 from the content selection module 182, wherethe content selection module 182 generates the content selectioninformation 194 based on a content selection information request fromthe content acquisition module 180 and where the content acquisitionmodule 180 generates the content selection information request based onthe collections request 132.

The content acquisition module 180 obtains source selection information196 based on the collections request 132. The source selectioninformation 196 includes one or more of candidate source identifiers, acontent profile, selected sources, source priority levels, andrecommended source access timing. For example, the content acquisitionmodule 180 receives the source selection information 196 from the sourceselection module 184, where the source selection module 184 generatesthe source selection information 196 based on a source selectioninformation request from the content acquisition module 180 and wherethe content acquisition module 180 generates the source selectioninformation request based on the collections request 132.

The content acquisition module 180 obtains acquisition timinginformation 200 based on the collections request 132. The acquisitiontiming information 200 includes one or more of recommended source accesstiming, confirmed source access timing, source access testing results,estimated velocity of content update's, content precious, timestamps,predicted time availability, required content acquisition triggers,content acquisition trigger detection indicators, and a duplicativeindicator with a pending content request. For example, the contentacquisition module 180 receives the acquisition timing information 200from the acquisition timing module 188, where the acquisition timingmodule 188 generates the acquisition timing information 200 based on anacquisition timing information request from the content acquisitionmodule 180 and where the content acquisition module 180 generates theacquisition timing information request based on the collections request132.

Having obtained the content selection information 194, the sourceselection information 196, and the acquisition timing information 200,the content acquisition module 180 issues a content request 126 to acontent source utilizing security information 198 from the contentsecurity module 186, where the content acquisition module 180 generatesthe content request 126 in accordance with the content selectioninformation 194, the source selection information 196, and theacquisition timing information 200. The security information 198includes one or more of source priority requirements, requester securityinformation, available security procedures, and security credentials fortrust and/or encryption. For example, the content acquisition module 180generates the content request 126 to request a particular content typein accordance with the content selection information 194 and to includesecurity parameters of the security information 198, initiates sendingof the content request 126 in accordance with the acquisition timinginformation 200, and sends the content request 126 to a particulartargeted content source in accordance with the source selectioninformation 196.

In response to receiving a content response 128, the content acquisitionmodule 180 determines the quality level of received content extractedfrom the content response 128. For example, the content acquisitionmodule 180 receives content quality information 204 from the contentquality module 192, where the content quality module 192 generates thequality level of the received content based on receiving a contentquality request from the content acquisition module 180 and where thecontent acquisition module 180 generates the content quality requestbased on content extracted from the content response 128. The contentquality information includes one or more of a content reliabilitythreshold range, a content accuracy threshold range, a desired contentquality level, a predicted content quality level, and a predicted levelof trust.

When the quality level is below a minimum desired quality thresholdlevel, the content acquisition module 180 facilitates acquisition offurther content. The facilitating includes issuing another contentrequest 126 to a same content source and/or to another content source toreceive and interpret further received content. When the quality levelis above the minimum desired quality threshold level, the contentacquisition module 180 issues a collections response 134 to therequester. The issuing includes processing the content in accordancewith a transformation approach to produce transformed content,generating the collections response 134 to include the transformedcontent, and sending the collections response 134 to the requester. Theprocessing of the content to produce the transformed content includesreceiving content transformation information 202 from the contenttransformation module 190, where the content transformation module 190transforms the content in accordance with the transformation approach toproduce the transformed content. The content transformation informationincludes a desired format, available formats, recommended formatting,the received content, transformation instructions, and the transformedcontent.

FIG. 5B is a logic diagram of an embodiment of a method for obtainingcontent within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-3, 4A-4C, 5A, and also FIG. 5B. The methodincludes step 210 where a processing module of one or more processingmodules of one or more computing devices of the computing systemreceives a collections request from the requester. The method continuesat step 212 where the processing module determines content selectioninformation. The determining includes interpreting the collectionsrequest to identify requirements of the content.

The method continues at step 214 for the processing module determinessource selection information. The determining includes interpreting thecollections request to identify and select one or more sources for thecontent to be collected. The method continues at step 216 for theprocessing module determines acquisition timing information. Thedetermining includes interpreting the collections request to identifytiming requirements for the acquisition of the content from the one ormore sources. The method continues at step 218 where the processingmodule issues a content request utilizing security information and inaccordance with one or more of the content selection information, thesource selection information, and the acquisition timing information.For example, the processing module issues the content request to the oneor more sources for the content in accordance with the contentrequirements, where the sending of the request is in accordance with theacquisition timing information.

The method continues at step 220 for the processing module determines acontent quality level for received content area the determining includesreceiving the content from the one or more sources, obtaining contentquality information for the received content based on a quality analysisof the received content. The method branches to step 224 when thecontent quality level is favorable and the method continues to step 222when the quality level is unfavorable. For example, the processingmodule determines that the content quality level is favorable when thecontent quality level is equal to or above a minimum quality thresholdlevel and determines that the content quality level is unfavorable whenthe content quality level is less than the minimum quality thresholdlevel.

When the content quality level is unfavorable, the method continues atstep 222 where the processing module facilitates acquisition and furthercontent. For example, the processing module issues further contentrequests and receives further content for analysis. When the contentquality level is favorable, the method continues at step 224 where theprocessing module issues a collections response to the requester. Theissuing includes generating the collections response and sending thecollections response to the requester. The generating of the collectionsresponse may include transforming the received content into transformedcontent in accordance with a transformation approach (e.g.,reformatting, interpreting absolute meaning and translating into anotherlanguage in accordance with the absolute meaning, etc.).

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 5C is a schematic block diagram of an embodiment of the querymodule 124 of FIG. 4A that includes an answer acquisition module 230, acontent requirements module 232 a source requirements module 234, acontent security module 236, an answer timing module 238, an answertransformation module 240, and an answer quality module 242. Generally,an embodiment of this invention presents solutions where the querymodule 124 supports responding to a query.

In an example of operation of the responding to the query, the answeracquisition module 230 receives a query request 136 from a requester.The answer acquisition module 230 obtains content requirementsinformation 248 based on the query request 136. The content requirementsinformation 248 includes one or more of content parameters, a desiredcontent type, a desired content source if any, a content type if any,candidate source identifiers, a content profile, and a question of thequery request 136. For example, the answer acquisition module 230receives the content requirements information 248 from the contentrequirements module 232, where the content requirements module 232generates the content requirements information 248 based on a contentrequirements information request from the answer acquisition module 230and where the answer acquisition module 230 generates the contentrequirements information request based on the query request 136.

The answer acquisition module 230 obtains source requirementsinformation 250 based on the query request 136. The source requirementsinformation 250 includes one or more of candidate source identifiers, acontent profile, a desired source parameter, recommended sourceparameters, source priority levels, and recommended source accesstiming. For example, the answer acquisition module 230 receives thesource requirements information 250 from the source requirements module234, where the source requirements module 234 generates the sourcerequirements information 250 based on a source requirements informationrequest from the answer acquisition module 230 and where the answeracquisition module 230 generates the source requirements informationrequest based on the query request 136.

The answer acquisition module 230 obtains answer timing information 254based on the query request 136. The answer timing information 254includes one or more of requested answer timing, confirmed answertiming, source access testing results, estimated velocity of contentupdates, content freshness, timestamps, predicted time available,requested content acquisition trigger, and a content acquisition triggerdetected indicator. For example, the answer acquisition module 230receives the answer timing information 254 from the answer timing module238, where the answer timing module 238 generates the answer timinginformation 254 based on an answer timing information request from theanswer acquisition module 230 and where the answer acquisition module230 generates the answer timing information request based on the queryrequest 136.

Having obtained the content requirements information 248, the sourcerequirements information 250, and the answer timing information 254, theanswer acquisition module 230 determines whether to issue an IEI request244 and/or a collections request 132 based on one or more of the contentrequirements information 248, the source requirements information 250,and the answer timing information 254. For example, the answeracquisition module 230 selects the IEI request 244 when an immediateanswer to a simple query request 136 is required and is expected to havea favorable quality level. As another example, the answer acquisitionmodule 230 selects the collections request 132 when a longer-term answeris required as indicated by the answer timing information to beforeand/or when the query request 136 has an unfavorable quality level.

When issuing the IEI request 244, the answer acquisition module 230generates the IEI request 244 in accordance with security information252 received from the content security module 236 and based on one ormore of the content requirements information 248, the sourcerequirements information 250, and the answer timing information 254.Having generated the IEI request 244, the answer acquisition module 230sends the IEI request 244 to at least one IEI module.

When issuing the collections request 132, the answer acquisition module230 generates the collections request 132 in accordance with thesecurity information 252 received from the content security module 236and based on one or more of the content requirements information 248,the source requirements information 250, and the answer timinginformation 254. Having generated the collections request 132, theanswer acquisition module 230 sends the collections request 132 to atleast one collections module. Alternatively, the answer acquisitionmodule 230 facilitate sending of the collections request 132 to one ormore various user devices (e.g., to access a subject matter expert).

The answer acquisition module 230 determines a quality level of areceived answer extracted from a collections response 134 and/or an IEIresponse 246. For example, the answer acquisition module 230 extractsthe quality level of the received answer from answer quality information258 received from the answer quality module 242 in response to an answerquality request from the answer acquisition module 230. When the qualitylevel is unfavorable, the answer acquisition module 230 facilitatesobtaining a further answer. The facilitation includes issuing at leastone of a further IEI request 244 and a further collections request 132to generate a further answer for further quality testing. When thequality level is favorable, the answer acquisition module 230 issues aquery response 140 to the requester. The issuing includes generating thequery response 140 based on answer transformation information 256received from the answer transformation module 240, where the answertransformation module 240 generates the answer transformationinformation 256 to include a transformed answer based on receiving theanswer from the answer acquisition module 230. The answer transformationinformation 250 6A further include the question, a desired format of theanswer, available formats, recommended formatting, received IEIresponses, transformation instructions, and transformed IEI responsesinto an answer.

FIG. 5D is a logic diagram of an embodiment of a method for providing aresponse to a query within a computing system. In particular, a methodis presented for use in conjunction with one or more functions andfeatures described in conjunction with FIGS. 1-3, 4A-4C, 5C, and alsoFIG. 5D. The method includes step 270 where a processing module of oneor more processing modules of one or more computing devices of thecomputing system receives a query request (e.g., a question) from arequester. The method continues at step 272 where the processing moduledetermines content requirements information. The determining includesinterpreting the query request to produce the content requirements. Themethod continues at step 274 where the processing module determinessource requirements information. The determining includes interpretingthe query request to produce the source requirements. The methodcontinues at step 276 where the processing module determines answertiming information. The determining includes interpreting the queryrequest to produce the answer timing information.

The method continues at step 278 the processing module determineswhether to issue an IEI request and/or a collections request. Forexample, the determining includes selecting the IEI request when theanswer timing information indicates that a simple one-time answer isappropriate. As another example, the processing module selects thecollections request when the answer timing information indicates thatthe answer is associated with a series of events over an event timeframe. When issuing the IEI request, the method continues at step 280where the processing module issues the IEI request to an IEI module. Theissuing includes generating the IEI request in accordance with securityinformation and based on one or more of the content requirementsinformation, the source requirements information, and the answer timinginformation.

When issuing the collections request, the method continues at step 282where the processing module issues the collections request to acollections module. The issuing includes generating the collectionsrequest in accordance with the security information and based on one ormore of the content requirements information, the source requirementsinformation, and the answer timing information. Alternatively, theprocessing module issues both the IEI request and the collectionsrequest when a satisfactory partial answer may be provided based on acorresponding IEI response and a further more generalized and specificanswer may be provided based on a corresponding collections response andassociated further IEI response.

The method continues at step 284 where the processing module determinesa quality level of a received answer. The determining includesextracting the answer from the collections response and/or the IEIresponse and interpreting the answer in accordance with one or more ofthe content requirements information, the source requirementsinformation, the answer timing information, and the query request toproduce the quality level. The method branches to step 288 when thequality level is favorable and the method continues to step 286 when thequality level is unfavorable. For example, the processing moduleindicates that the quality level is favorable when the quality level isequal to or greater than a minimum answer quality threshold level. Asanother example, the processing module indicates that the quality levelis unfavorable when the quality level is less than the minimum answerquality threshold level.

When the quality level is unfavorable, the method continues at step 286where the processing module obtains a further answer. The obtainingincludes at least one of issuing a further IEI request and a furthercollections request to facilitate obtaining of a further answer forfurther answer quality level testing as the method loops back to step270. When the quality level is favorable, the method continues at step288 where the processing module issues a query response to therequester. The issuing includes transforming the answer into atransformed answer in accordance with an answer transformation approach(e.g., formatting, further interpretations of the virtual question inlight of the answer and further knowledge) and sending the transformedanswer to the requester as the query response.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 5E is a schematic block diagram of an embodiment of the identigenentigen intelligence (IEI) module 122 of FIG. 4A that includes a contentingestion module 300, an element identification module 302, andinterpretation module 304, and answer resolution module 306, and an IEIcontrol module 308. Generally, an embodiment of this invention presentssolutions where the IEI module 122 supports interpreting content toproduce knowledge that may be utilized to answer questions.

In an example of operation of the producing and utilizing of theknowledge, the content ingestion module 300 generates formatted content314 based on question content 312 and/or source content 310, where theIEI module 122 receives an IEI request 244 that includes the questioncontent 312 and the IEI module 122 receives a collections response 134that includes the source content 310. The source content 310 includescontent from a source extracted from the collections response 134. Thequestion content 312 includes content extracted from the IEI request 244(e.g., content paired with a question). The content ingestion module 300generates the formatted content 314 in accordance with a formattingapproach (e.g., creating proper sentences from words of the content).The formatted content 314 includes modified content that is compatiblewith subsequent element identification (e.g., complete sentences,combinations of words and interpreted sounds and/or inflection cues withtemporal associations of words).

The element identification module 302 processes the formatted content314 based on element rules 318 and an element list 332 to produceidentified element information 340. Rules 316 includes the element rules318 (e.g., match, partial match, language translation, etc.). Lists 330includes the element list 332 (e.g., element ID, element context ID,element usage ID, words, characters, symbols etc.). The IEI controlmodule 308 may provide the rules 316 and the lists 330 by accessingstored data 360 from a memory associated with the IEI module 122.Generally, an embodiment of this invention presents solutions where thestored data 360 may further include one or more of a descriptivedictionary, categories, representations of element sets, element list,sequence data, pending questions, pending request, recognized elements,unrecognized elements, errors, etc.

The identified element information 340 includes one or more ofidentifiers of elements identified in the formatted content 314, mayinclude ordering and/or sequencing and grouping information. Forexample, the element identification module 302 compares elements of theformatted content 314 to known elements of the element list 332 toproduce identifiers of the known elements as the identified elementinformation 340 in accordance with the element rules 318. Alternatively,the element identification module 302 outputs un-identified elementinformation 342 to the IEI control module 308, where the un-identifiedelement information 342 includes temporary identifiers for elements notidentifiable from the formatted content 314 when compared to the elementlist 332.

The interpretation module 304 processes the identified elementinformation 340 in accordance with interpretation rules 320 (e.g.,potentially valid permutations of various combinations of identifiedelements), question information 346 (e.g., a question extracted from theIEI request to hundred 44 which may be paired with content associatedwith the question), and a groupings list 334 (e.g., representations ofassociated groups of representations of things, a set of elementidentifiers, valid element usage IDs in accordance with similar, anelement context, permutations of sets of identifiers for possibleinterpretations of a sentence or other) to produce interpretedinformation 344. The interpreted information 344 includes potentiallyvalid interpretations of combinations of identified elements. Generally,an embodiment of this invention presents solutions where theinterpretation module 304 supports producing the interpreted information344 by considering permutations of the identified element information340 in accordance with the interpretation rules 320 and the groupingslist 334.

The answer resolution module 306 processes the interpreted information344 based on answer rules 322 (e.g., guidance to extract a desiredanswer), the question information 346, and inferred question information352 (e.g., posed by the IEI control module or analysis of generalcollections of content or refinement of a stated question from arequest) to produce preliminary answers 354 and an answer quality level356. The answer generally lies in the interpreted information 344 asboth new content received and knowledge based on groupings list 334generated based on previously received content. The preliminary answers354 includes an answer to a stated or inferred question that subjectfurther refinement. The answer quality level 356 includes adetermination of a quality level of the preliminary answers 354 based onthe answer rules 322. The inferred question information 352 may furtherbe associated with time information 348, where the time informationincludes one or more of current real-time, a time reference associatedwith entity submitting a request, and a time reference of a collectionsresponse.

When the IEI control module 308 determines that the answer quality level356 is below an answer quality threshold level, the IEI control module308 facilitates collecting of further content (e.g., by issuing acollections request 132 and receiving corresponding collectionsresponses 134 for analysis). When the answer quality level 356 comparesfavorably to the answer quality threshold level, the IEI control module308 issues an IEI response 246 based on the preliminary answers 354.When receiving training information 358, the IEI control module 308facilitates updating of one or more of the lists 330 and the rules 316and stores the updated list 330 and the updated rules 316 in thememories as updated stored data 360.

FIG. 5F is a logic diagram of an embodiment of a method for analyzingcontent within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-3, 4A-4C, 5E, and also FIG. 5F. The methodincludes step 370 where a processing module of one or more processingmodules of one or more computing devices of the computing systemfacilitates updating of one or more rules and lists based on one or moreof received training information and received content. For example, theprocessing module updates rules with received rules to produce updatedrules and updates element lists with received elements to produceupdated element lists. As another example, the processing moduleinterprets the received content to identify a new word for at leasttemporary inclusion in the updated element list.

The method continues at step 372 where the processing module transformsat least some of the received content into formatted content. Forexample, the processing module processes the received content inaccordance with a transformation approach to produce the formattedcontent, where the formatted content supports compatibility withsubsequent element identification (e.g., typical sentence structures ofgroups of words).

The method continues at step 374 where the processing module processesthe formatted content based on the rules and the lists to produceidentified element information and/or an identified element information.For example, the processing module compares the formatted content toelement lists to identify a match producing identifiers for identifiedelements or new identifiers for unidentified elements when there is nomatch.

The method continues at step 376 with a processing module processes theidentified element information based on rules, the lists, and questioninformation to produce interpreted information. For example, theprocessing module compares the identified element information toassociated groups of representations of things to generate potentiallyvalid interpretations of combinations of identified elements.

The method continues at step 378 where the processing module processesthe interpreted information based on the rules, the questioninformation, and inferred question information to produce preliminaryanswers. For example, the processing module matches the interpretedinformation to one or more answers (e.g., embedded knowledge based on afact base built from previously received content) with highestcorrectness likelihood levels that is subject to further refinement.

The method continues at step 380 where the processing module generatesan answer quality level based on the preliminary answers, the rules, andthe inferred question information. For example, the processing modulepredicts the answer correctness likelihood level based on the rules, theinferred question information, and the question information. The methodbranches to step 384 when the answer quality level is favorable and themethod continues to step 382 when the answer quality level isunfavorable. For example, the generating of the answer quality levelfurther includes the processing module indicating that the answerquality level is favorable when the answer quality level is greater thanor equal to a minimum answer quality threshold level. As anotherexample, the generating of the answer quality level further includes theprocessing module indicating that the answer quality level isunfavorable when the answer quality level is less than the minimumanswer quality threshold level.

When the answer quality level is unfavorable, the method continues atstep 382 where the processing module facilitates gathering clarifyinginformation. For example, the processing module issues a collectionsrequest to facilitate receiving further content and or request questionclarification from a question requester. When the answer quality levelis favorable, the method continues at step 384 where the processingmodule issues a response that includes one or more answers based on thepreliminary answers and/or further updated preliminary answers based ongathering further content. For example, the processing module generatesa response that includes one or more answers and the answer qualitylevel and issues the response to the requester.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 6A is a schematic block diagram of an embodiment of the elementidentification module 302 of FIG. 5A and the interpretation module 304of FIG. 5A. The element identification module 302 includes an elementmatching module 400 and an element grouping module 402. Theinterpretation module 304 includes a grouping matching module 404 and agrouping interpretation module 406. Generally, an embodiment of thisinvention presents solutions where the element identification module 302supports identifying potentially valid permutations of groupings ofelements while the interpretation module 304 interprets the potentiallyvalid permutations of groupings of elements to produce interpretedinformation that includes the most likely of groupings based on aquestion.

In an example of operation of the identifying of the potentially validpermutations of groupings of elements, when matching elements of theformatted content 314, the element matching module 400 generates matchedelements 412 (e.g., identifiers of elements contained in the formattedcontent 314) based on the element list 332. For example, the elementmatching module 400 matches a received element to an element of theelement list 332 and outputs the matched elements 412 to include anidentifier of the matched element. When finding elements that areunidentified, the element matching module 400 outputs un-recognizedwords information 408 (e.g., words not in the element list 332, maytemporarily add) as part of un-identified element information 342. Forexample, the element matching module 400 indicates that a match cannotbe made between a received element of the formatted content 314,generates the unrecognized words info 408 to include the receivedelement and/or a temporary identifier, and issues and updated elementlist 414 that includes the temporary identifier and the correspondingunidentified received element.

The element grouping module 402 analyzes the matched elements 412 inaccordance with element rules 318 to produce grouping error information410 (e.g., incorrect sentence structure indicators) when a structuralerror is detected. The element grouping module 402 produces identifiedelement information 340 when favorable structure is associated with thematched elements in accordance with the element rules 318. Theidentified element information 340 may further include groupinginformation of the plurality of permutations of groups of elements(e.g., several possible interpretations), where the grouping informationincludes one or more groups of words forming an associated set and/orsuper-group set of two or more subsets when subsets share a common coreelement.

In an example of operation of the interpreting of the potentially validpermutations of groupings of elements to produce the interpretedinformation, the grouping matching module 404 analyzes the identifiedelement information 340 in accordance with a groupings list 334 toproduce validated groupings information 416. For example, the groupingmatching module 404 compares a grouping aspect of the identified elementinformation 340 (e.g., for each permutation of groups of elements ofpossible interpretations), generates the validated groupings information416 to include identification of valid permutations aligned with thegroupings list 334. Alternatively, or in addition to, the groupingmatching module 404 generates an updated groupings list 418 whendetermining a new valid grouping (e.g., has favorable structure andinterpreted meaning) that is to be added to the groupings list 334.

The grouping interpretation module 406 interprets the validatedgroupings information 416 based on the question information 346 and inaccordance with the interpretation rules 320 to produce interpretedinformation 344 (e.g., most likely interpretations, next most likelyinterpretations, etc.). For example, the grouping interpretation module406 obtains context, obtains favorable historical interpretations,processes the validated groupings based on interpretation rules 320,where each interpretation is associated with a correctness likelihoodlevel.

FIG. 6B is a logic diagram of an embodiment of a method for interpretinginformation within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-3, 4A-4C, 5E-5F, 6A, and also FIG.6B. The method includes step 430 where a processing module of one ormore processing modules of one or more computing devices of thecomputing system analyzes formatted content. For example, the processingmodule attempt to match a received element of the formatted content toone or more elements of an elements list. When there is no match, themethod branches to step 434 and when there is a match, the methodcontinues to step 432. When there is a match, the method continues atstep 432 for the processing module outputs matched elements (e.g., toinclude the matched element and/or an identifier of the matchedelement). When there is no match, the method continues at step 434 wherethe processing module outputs unrecognized words (e.g., elements and/ora temporary identifier for the unmatched element).

The method continues at step 436 for the processing module analyzesmatched elements. For example, the processing module attempt to match adetected structure of the matched elements (e.g., chained elements as ina received sequence) to favorable structures in accordance with elementrules. The method branches to step 440 when the analysis is unfavorableand the method continues to step 438 when the analysis is favorable.When the analysis is favorable matching a detected structure to thefavorable structure of the element rules, the method continues at step438 where the processing module outputs identified element information(e.g., an identifier of the favorable structure, identifiers of each ofthe detected elements). When the analysis is unfavorable matching adetected structure to the favorable structure of the element rules, themethod continues at step 440 where the processing module outputsgrouping error information (e.g., a representation of the incorrectstructure, identifiers of the elements of the incorrect structure, atemporary new identifier of the incorrect structure).

The method continues at step 442 for the processing module analyzes theidentified element information to produce validated groupingsinformation. For example, the processing module compares a groupingaspect of the identified element information and generates the validatedgroupings information to include identification of valid permutationsthat align with the groupings list. Alternatively, or in addition to,the processing module generates an updated groupings list whendetermining a new valid grouping.

The method continues at step 444 where the processing module interpretsthe validated groupings information to produce interpreted information.For example, the processing module obtains one or more of context andhistorical interpretations and processes the validated groupings basedon interpretation rules to generate the interpreted information, whereeach interpretation is associated with a correctness likelihood level(e.g., a quality level).

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 6C is a schematic block diagram of an embodiment of the answerresolution module 306 of FIG. 5A that includes an interim answer module460, and answer prioritization module 462, and a preliminary answerquality module 464. Generally, an embodiment of this invention presentssolutions where the answer resolution module 306 supports producing ananswer for interpreted information 344.

In an example of operation of the providing of the answer, the interimanswer module 460 analyzes the interpreted information 344 based onquestion information 346 and inferred question information 352 toproduce interim answers 466 (e.g., answers to stated and/or inferredquestions without regard to rules that is subject to furtherrefinement). The answer prioritization module 462 analyzes the interimanswers 466 based on answer rules 322 to produce preliminary answer 354.For example, the answer prioritization module 462 identifies allpossible answers from the interim answers 466 that conform to the answerrules 322.

The preliminary answer quality module 464 analyzes the preliminaryanswers 354 in accordance with the question information 346, theinferred question information 352, and the answer rules 322 to producean answer quality level 356. For example, for each of the preliminaryanswers 354, the preliminary answer quality module 464 may compare a fitof the preliminary answer 354 to a corresponding previous answer andquestion quality level, calculate the answer quality level 356 based ona level of conformance to the answer rules 322, calculate the answerquality level 356 based on alignment with the inferred questioninformation 352, and determine the answer quality level 356 based on aninterpreted correlation with the question information 346.

FIG. 6D is a logic diagram of an embodiment of a method for producing ananswer within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-3, 4A-4C, 5E-5F, 6C, and also FIG. 6D. Themethod includes step 480 where a processing module of one or moreprocessing modules of one or more computing devices of the computingsystem analyzes received interpreted information based on questioninformation and inferred question information to produce one or moreinterim answers. For example, the processing module generates potentialanswers based on patterns consistent with previously produced knowledgeand likelihood of correctness. The method continues at step 482 wherethe processing module analyzes the one or more interim answers based onanswer rules to produce preliminary answers. For example, the processingmodule identifies all possible answers from the interim answers thatconform to the answer rules. The method continues at step 484 for theprocessing module analyzes the preliminary answers in accordance withthe question information, the inferred question information, and theanswer rules to produce an answer quality level. For example, for eachof the elementary answers, the processing module may compare a fit ofthe preliminary answer to a corresponding previous answer-and-answerquality level, calculate the answer quality level based on performanceto the answer rules, calculate answer quality level based on alignmentwith the inferred question information, and determine the answer qualitylevel based on interpreted correlation with the question information.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 7A is an information flow diagram for interpreting informationwithin a computing system, where sets of entigens 504 are interpretedfrom sets of identigens 502 which are interpreted from sentences ofwords 500. Such identigen entigen intelligence (IEI) processing of thewords (e.g., to IEI process) includes producing one or more of interimknowledge, a preliminary answer, and an answer quality level. Forexample, the IEI processing includes identifying permutations ofidentigens of a phrase of a sentence (e.g., interpreting humanexpressions to produce identigen groupings for each word of ingestedcontent), reducing the permutations of identigens (e.g., utilizing rulesto eliminate unfavorable permutations), mapping the reduced permutationsof identigens to at least one set of entigens (e.g., most likelyidentigens become the entigens) to produce the interim knowledge,processing the knowledge in accordance with a knowledge base (e.g.,comparing the set of entigens to the knowledge base) to produce apreliminary answer, and generating the answer quality level based on thepreliminary answer for a corresponding domain.

Human expressions are utilized to portray facts and fiction about thereal world. The real-world includes items, actions, and attributes. Thehuman expressions include textual words, textual symbols, images, andother sensorial information (e.g., sounds). It is known that many words,within a given language, can mean different things based on groupingsand orderings of the words. For example, the sentences of words 500 caninclude many different forms of sentences that mean vastly differentthings even when the words are very similar.

The present invention presents solutions where the computing system 10supports producing a computer-based representation of a truest meaningpossible of the human expressions given the way that multitudes of humanexpressions relate to these meanings. As a first step of the flowdiagram to transition from human representations of things to a mostprecise computer representation of the things, the computer identifiesthe words, phrases, sentences, etc. from the human expressions toproduce the sets of identigens 502. Each identigen includes anidentifier of their meaning and an identifier of an instance for eachpossible language, culture, etc. For example, the words car andautomobile share a common meaning identifier but have different instanceidentifiers since they are different words and are spelled differently.As another example, the word duck is associated both with a bird and anaction to elude even though they are spelled the same. In this examplethe bird duck has a different meaning than the elude duck and as sucheach has a different meaning identifier of the corresponding identigens.

As a second step of the flow diagram to transition from humanrepresentations of things to the most precise computer representation ofthe things, the computer extracts meaning from groupings of theidentified words, phrases, sentences, etc. to produce the sets ofentigens 504. Each entigen includes an identifier of a singleconceivable and perceivable thing in space and time (e.g., independentof language and other aspects of the human expressions). For example,the words car and automobile are different instances of the same meaningand point to a common shared entigen. As another example, the word duckfor the bird meaning has an associated unique entigen that is differentthan the entigen for the word duck for the elude meaning.

As a third step of the flow diagram to transition from human expressionsof things to the most precise computer representation of the things, thecomputer reasons facts from the extracted meanings. For example, thecomputer maintains a fact-based of the valid meanings from the validgroupings or sets of entigens so as to support subsequent inferences,deductions, rationalizations of posed questions to produce answers thatare aligned with a most factual view. As time goes on, and as an entigenhas been identified, it can encounter an experience transformations intime, space, attributes, actions, and words which are used to identifyit without creating contradictions or ever losing its identity.

FIG. 7B is a relationship block diagram illustrating an embodiment ofrelationships between things 510 and representations of things 512within a computing system. The things 510 includes conceivable andperceivable things including actions 522, items 524, and attributes 526.The representation of things 512 includes representations of things usedby humans 514 and representation of things used by of computing devices516 of embodiments of the present invention. The things 510 relates tothe representations of things used by humans 514 where the inventionpresents solutions where the computing system 10 supports mapping therepresentations of things used by humans 514 to the representations ofthings used by computing devices 516, where the representations ofthings used by computing devices 516 map back to the things 510.

The representations of things used by humans 514 includes textual words528, textual symbols 530, images (e.g., non-textual) 532, and othersensorial information 534 (e.g., sounds, sensor data, electrical fields,voice inflections, emotion representations, facial expressions,whistles, etc.). The representations of things used by computing devices516 includes identigens 518 and entigens 520. The representations ofthings used by humans 514 maps to the identigens 518 and the identigens518 map to the entigens 520. The entigens 520 uniquely maps back to thethings 510 in space and time, a truest meaning the computer is lookingfor to create knowledge and answer questions based on the knowledge.

To accommodate the mapping of the representations of things used byhumans 514 to the identigens 518, the identigens 518 is partitioned intoactenyms 544 (e.g., actions), itenyms 546 (e.g., items), attrenyms 548(e.g., attributes), and functionals 550 (e.g., that join and/ordescribe). Each of the actenyms 544, itenyms 546, and attrenyms 548 maybe further classified into singulatums 552 (e.g., identify one uniqueentigen) and pluratums 554 (e.g., identify a plurality of entigens thathave similarities).

Each identigen 518 is associated with an identigens identifier (IDN)536. The IDN 536 includes a meaning identifier (ID) 538 portion, aninstance ID 540 portion, and a type ID 542 portion. The meaning ID 538includes an identifier of common meaning. The instance ID 540 includesan identifier of a particular word and language. The type ID 542includes one or more identifiers for actenyms, itenyms, attrenyms,singulatums, pluratums, a time reference, and any other reference todescribe the IDN 536. The mapping of the representations of things usedby humans 514 to the identigens 518 by the computing system of thepresent invention includes determining the identigens 518 in accordancewith logic and instructions for forming groupings of words.

Generally, an embodiment of this invention presents solutions where theidentigens 518 map to the entigens 520. Multiple identigens may map to acommon unique entigen. The mapping of the identigens 518 to the entigens520 by the computing system of the present invention includesdetermining entigens in accordance with logic and instructions forforming groupings of identigens.

FIG. 7C is a diagram of an embodiment of a synonym words table 570within a computing system, where the synonym words table 570 includesmultiple fields including textual words 572, identigens 518, andentigens 520. The identigens 518 includes fields for the meaningidentifier (ID) 538 and the instance ID 540. The computing system of thepresent invention may utilize the synonym words table 570 to map textualwords 572 to identigens 518 and map the identigens 518 to entigens 520.For example, the words car, automobile, auto, bil (Swedish), carro(Spanish), and bil (Danish) all share a common meaning but are differentinstances (e.g., different words and languages). The words map to acommon meaning ID but to individual unique instant identifiers. Each ofthe different identigens map to a common entigen since they describe thesame thing.

FIG. 7D is a diagram of an embodiment of a polysemous words table 576within a computing system, where the polysemous words table 576 includesmultiple fields including textual words 572, identigens 518, andentigens 520. The identigens 518 includes fields for the meaningidentifier (ID) 538 and the instance ID 540. The computing system of thepresent invention may utilize the polysemous words table 576 to maptextual words 572 to identigens 518 and map the identigens 518 toentigens 520. For example, the word duck maps to four differentidentigens since the word duck has four associated different meanings(e.g., bird, fabric, to submerge, to elude) and instances. Each of theidentigens represent different things and hence map to four differententigens.

FIG. 7E is a diagram of an embodiment of transforming words intogroupings within a computing system that includes a words table 580, agroupings of words section to validate permutations of groupings, and agroupings table 584 to capture the valid groupings. The words table 580includes multiple fields including textual words 572, identigens 518,and entigens 520. The identigens 518 includes fields for the meaningidentifier (ID) 538, the instance ID 540, and the type ID 542. Thecomputing system of the present invention may utilize the words table580 to map textual words 572 to identigens 518 and map the identigens518 to entigens 520. For example, the word pilot may refer to a flyerand the action to fly. Each meaning has a different identigen anddifferent entigen.

The computing system the present invention may apply rules to the fieldsof the words table 580 to validate various groupings of words. Thosethat are invalid are denoted with a “X” while those that are valid areassociated with a check mark. For example, the grouping “pilot Tom” isinvalid when the word pilot refers to flying and Tom refers to a person.The identigen combinations for the flying pilot and the person Tom aredenoted as invalid by the rules. As another example, the grouping “pilotTom” is valid when the word pilot refers to a flyer and Tom refers tothe person. The identigen combinations for the flyer pilot and theperson Tom are denoted as valid by the rules.

The groupings table 584 includes multiple fields including grouping ID586, word strings 588, identigens 518, and entigens 520. The computingsystem of the present invention may produce the groupings table 584 as astored fact base for valid and/or invalid groupings of words identifiedby their corresponding identigens. For example, the valid grouping“pilot Tom” referring to flyer Tom the person is represented with agrouping identifier of 3001 and identity and identifiers 150.001 and457.001. The entigen field 520 may indicate associated entigens thatcorrespond to the identigens. For example, entigen e717 corresponds tothe flyer pilot meaning and entigen e61 corresponds to the time theperson meaning. Alternatively, or in addition to, the entigen field 520may be populated with a single entigen identifier (ENI).

The word strings field 588 may include any number of words in a string.Different ordering of the same words can produce multiple differentstrings and even different meanings and hence entigens. More broadly,each entry (e.g., role) of the groupings table 584 may refer togroupings of words, two or more word strings, an idiom, just identigens,just entigens, and/or any combination of the preceding elements. Eachentry has a unique grouping identifier. An idiom may have a uniquegrouping ID and include identifiers of original word identigens andreplacing identigens associated with the meaning of the idiom not justthe meaning of the original words. Valid groupings may still haveambiguity on their own and may need more strings and/or context toselect a best fit when interpreting a truest meaning of the grouping.

FIG. 8A is a data flow diagram for accumulating knowledge within acomputing system, where a computing device, at a time=tO, ingests andprocesses facts 598 at a step 590 based on rules 316 and fact baseinformation 600 to produce groupings 602 for storage in a fact base 592(e.g., words, phrases, word groupings, identigens, entigens, qualitylevels). The facts 598 may include information from books, archive data,Central intelligence agency (CIA) world fact book, trusted content, etc.The ingesting may include filtering to organize and promote better validgroupings detection (e.g., considering similar domains together). Thegroupings 602 includes one or more of groupings identifiers, identigenidentifiers, entigen identifiers, and estimated fit quality levels. Theprocessing step 590 may include identifying identigens from words of thefacts 598 in accordance with the rules 316 and the fact base info 600and identifying groupings utilizing identigens in accordance with rules316 and fact base info 600.

Subsequent to ingestion and processing of the facts 598 to establish thefact base 592, at a time=μl+, the computing device ingests and processesnew content 604 at a step 594 in accordance with the rules 316 and thefact base information 600 to produce preliminary grouping 606. The newcontent may include updated content (e.g., timewise) from periodicals,newsfeeds, social media, etc. The preliminary grouping 606 includes oneor more of preliminary groupings identifiers, preliminary identigenidentifiers, preliminary entigen identifiers, estimated fit qualitylevels, and representations of unidentified words.

The computing device validates the preliminary groupings 606 at a step596 based on the rules 316 and the fact base info 600 to produce updatedfact base info 608 for storage in the fact base 592. The validatingincludes one or more of reasoning a fit of existing fact base info 600with the new preliminary grouping 606, discarding preliminary groupings,updating just time frame information associated with an entry of theexisting fact base info 600 (e.g., to validate knowledge for thepresent), creating new entigens, and creating a median entigen tosummarize portions of knowledge within a median indicator as a qualitylevel indicator (e.g., suggestive not certain).

Storage of the updated fact base information 608 captures patterns thatdevelop by themselves instead of searching for patterns as in prior artartificial intelligence systems. Growth of the fact base 592 enablessubsequent reasoning to create new knowledge including deduction,induction, inference, and inferential sentiment (e.g., a chain ofsentiment sentences). Examples of sentiments includes emotion, beliefs,convictions, feelings, judgments, notions, opinions, and views.

FIG. 8B is a diagram of an embodiment of a groupings table 620 within acomputing system. The groupings table 620 includes multiple fieldsincluding grouping ID 586, word strings 588, an IF string 622 and a THENstring 624. Each of the fields for the IF string 622 and the THEN string624 includes fields for an identigen (IDN) string 626, and an entigen(ENI) string 628. The computing system of the present invention mayproduce the groupings table 620 as a stored fact base to enable IF THENbased inference to generate a new knowledge inference 630.

As a specific example, grouping 5493 points out the logic of IF someonehas a tumor, THEN someone is sick and the grouping 5494 points of thelogic that IF someone is sick, THEN someone is sad. As a result ofutilizing inference, the new knowledge inference 630 may producegrouping 5495 where IF someone has a tumor, THEN someone is possibly sad(e.g., or is sad).

FIG. 8C is a data flow diagram for answering questions utilizingaccumulated knowledge within a computing system, where a computingdevice ingests and processes question information 346 at a step 640based on rules 316 and fact base info 600 from a fact base 592 toproduce preliminary grouping 606. The ingesting and processing questionsstep 640 includes identifying identigens from words of a question inaccordance with the rules 316 and the fact base information 600 and mayalso include identifying groupings from the identified identigens inaccordance with the rules 316 and the fact base information 600.

The computing device validates the preliminary grouping 606 at a step596 based on the rules 316 and the fact base information 600 to produceidentified element information 340. For example, the computing devicereasons fit of existing fact base information with new preliminarygroupings 606 to produce the identified element information 340associated with highest quality levels. The computing device interpretsa question of the identified element information 340 at a step 642 basedon the rules 316 and the fact base information 600. The interpreting ofthe question may include separating new content from the question andreducing the question based on the fact base information 600 and the newcontent.

The computing device produces preliminary answers 354 from theinterpreted information 344 at a resolve answer step 644 based on therules 316 and the fact base information 600. For example, the computingdevice compares the interpreted information 344 two the fact baseinformation 600 to produce the preliminary answers 354 with highestquality levels utilizing one or more of deduction, induction,inferencing, and applying inferential sentiments logic. Alternatively,or in addition to, the computing device may save new knowledgeidentified from the question information 346 to update the fact base592.

FIG. 8D is a data flow diagram for answering questions utilizinginterference within a computing system that includes a groupings table648 and the resolve answer step 644 of FIG. 8C. The groupings table 648includes multiple fields including fields for a grouping (GRP)identifier (ID) 586, word strings 588, an identigen (IDN) string 626,and an entigen (ENI) 628. The groupings table 648 may be utilized tobuild a fact base to enable resolving a future question into an answer.For example, the grouping 8356 notes knowledge that Michael sleeps eighthours and grouping 8357 notes that Michael usually starts to sleep at 11PM.

In a first question example that includes a question “Michaelsleeping?”, the resolve answer step 644 analyzes the question from theinterpreted information 344 in accordance with the fact base information600, the rules 316, and a real-time indicator that the current time is 1AM to produce a preliminary answer of “possibly YES” when inferring thatMichael is probably sleeping at 1 AM when Michael usually startssleeping at 11 PM and Michael usually sleeps for a duration of eighthours.

In a second question example that includes the question “Michaelsleeping?”, the resolve answer step 644 analyzes the question from theinterpreted information 344 in accordance with the fact base information600, the rules 316, and a real-time indicator that the current time isnow 11 AM to produce a preliminary answer of “possibly NO” wheninferring that Michael is probably not sleeping at 11 AM when Michaelusually starts sleeping at 11 PM and Michael usually sleeps for aduration of eight hours.

FIG. 8E is a relationship block diagram illustrating another embodimentof relationships between things and representations of things within acomputing system. While things in the real world are described withwords, it is often the case that a particular word has multiple meaningsin isolation. Interpreting the meaning of the particular word may hingeon analyzing how the word is utilized in a phrase, a sentence, multiplesentences, paragraphs, and even whole documents or more. Describing andstratifying the use of words, word types, and possible meanings help ininterpreting a true meaning.

Humans utilize textual words 528 to represent things in the real world.Quite often a particular word has multiple instances of differentgrammatical use when part of a phrase of one or more sentences. Thegrammatical use 649 of words includes the nouns and the verbs, and alsoincludes adverbs, adjectives, pronouns, conjunctions, prepositions,determiners, exclamations, etc.

As an example of multiple grammatical use, the word “bat” in the Englishlanguage can be utilized as a noun or a verb. For instance, whenutilized as a noun, the word “bat” may apply to a baseball bat or mayapply to a flying “bat.” As another instance, when utilized as a verb,the word “bat” may apply to the action of hitting or batting an object,i.e., “bat the ball.”

To stratify word types by use, the words are associated with a word type(e.g., type identifier 542). The word types include objects (e.g., items524), characteristics (e.g., attributes 526), actions 522, and thefunctionals 550 for joining other words and describing words. Forexample, when the word “bat” is utilized as a noun, the word isdescribing the object of either the baseball bat or the flying bat. Asanother example, when the word “bat” is utilized as a verb, the word isdescribing the action of hitting.

To determine possible meanings, the words, by word type, are mapped toassociative meanings (e.g., identigens 718). For each possibleassociative meaning, the word type is documented with the meaning andfurther with an identifier (ID) of the instance (e.g., an identigenidentifier).

For the example of the word “bat” when utilized as a noun for thebaseball bat, a first identigen identifier 536-1 includes a type ID542-1 associated with the object 524, an instance ID 540-1 associatedwith the first identigen identifier (e.g., unique for the baseball bat),and a meaning ID 538-1 associated with the baseball bat. For the exampleof the word “bat” when utilized as a noun for the flying bat, a secondidentigen identifier 536-2 includes a type ID 542-1 associated with theobject 524, an instance ID 540-2 associated with the second identigenidentifier (e.g., unique for the flying bat), and a meaning ID 538-2associated with the flying bat. For the example of the word “bat” whenutilized as a verb for the bat that hits, a third identigen identifier536-2 includes a type ID 542-2 associated with the actions 522, aninstance ID 540-3 associated with the third identigen identifier (e.g.,unique for the bat that hits), and a meaning ID 538-3 associated withthe bat that hits.

With the word described by a type and possible associative meanings, acombination of full grammatical use of the word within the phrase etc.,application of rules, and utilization of an ever-growing knowledge basethat represents knowledge by linked entigens, the absolute meaning(e.g., entigen 520) of the word is represented as a unique entigen. Forexample, a first entigen e1 represents the absolute meaning of abaseball bat (e.g., a generic baseball bat not a particular baseball batthat belongs to anyone), a second entigen e2 represents the absolutemeaning of the flying bat (e.g., a generic flying bat not a particularflying bat), and a third entigen e3 represents the absolute meaning ofthe verb bat (e.g., to hit).

An embodiment of methods to ingest text to produce absolute meanings forstorage in a knowledge base are discussed in greater detail withreference to FIGS. 8F-H. Those embodiments further discuss thediscerning of the grammatical use, the use of the rules, and theutilization of the knowledge base to definitively interpret the absolutemeaning of a string of words.

Another embodiment of methods to respond to a query to produce an answerbased on knowledge stored in the knowledge base are discussed in greaterdetail with reference to FIGS. 8J-L. Those embodiments further discussthe discerning of the grammatical use, the use of the rules, and theutilization of the knowledge base to interpret the query. The queryinterpretation is utilized to extract the answer from the knowledge baseto facilitate forming the query response.

FIGS. 8F and 8G are schematic block diagrams of another embodiment of acomputing system that includes the content ingestion module 300 of FIG.5E, the element identification module 302 of FIG. 5E, the interpretationmodule 304 of FIG. 5E, the IEI control module 308 of FIG. 5E, and the SSmemory 96 of FIG. 2. Generally, an embodiment of this invention providespresents solutions where the computing system 10 supports processingcontent to produce knowledge for storage in a knowledge base.

The processing of the content to produce the knowledge includes a seriesof steps. For example, a first step includes identifying words of aningested phrase to produce tokenized words. As depicted in FIG. 8F, aspecific example of the first step includes the content ingestion module300 comparing words of source content 310 to dictionary entries toproduce formatted content 314 that includes identifiers of known words.Alternatively, when a comparison is unfavorable, the temporaryidentifier may be assigned to an unknown word. For instance, the contentingestion module 300 produces identifiers associated with the words“the”, “black”, “bat”, “eats”, and “fruit” when the ingested phraseincludes “The black bat eats fruit”, and generates the formatted content314 to include the identifiers of the words.

A second step of the processing of the content to produce the knowledgeincludes, for each tokenized word, identifying one or more identigensthat correspond the tokenized word, where each identigen describes oneof an object, a characteristic, and an action. As depicted in FIG. 8F, aspecific example of the second step includes the element identificationmodule 302 performing a look up of identigen identifiers, utilizing anelement list 332 and in accordance with element rules 318, of the one ormore identigens associated with each tokenized word of the formattedcontent 314 to produce identified element information 340.

A unique identifier is associated with each of the potential object, thecharacteristic, and the action (OCA) associated with the tokenized word(e.g. sequential identigens). For instance, the element identificationmodule 302 identifies a functional symbol for “the”, identifies a singleidentigen for “black”, identifies two identigens for “bat” (e.g.,baseball bat and flying bat), identifies a single identigen for “eats”,and identifies a single identigen for “fruit.” When at least onetokenized word is associated with multiple identigens, two or morepermutations of sequential combinations of identigens for each tokenizedword result. For example, when “bat” is associated with two identigens,two permutations of sequential combinations of identigens result for theingested phrase.

A third step of the processing of the content to produce the knowledgeincludes, for each permutation of sequential combinations of identigens,generating a corresponding equation package (i.e., candidateinterpretation), where the equation package includes a sequentiallinking of pairs of identigens (e.g., relationships), where eachsequential linking pairs a preceding identigen to a next identigen, andwhere an equation element describes a relationship between pairedidentigens (OCAs) such as describes, acts on, is a, belongs to, did, didto, etc. Multiple OCAs occur for a common word when the word hasmultiple potential meanings (e.g., a baseball bat, a flying bat).

As depicted in FIG. 8F, a specific example of the third step includesthe interpretation module 304, for each permutation of identigens ofeach tokenized word of the identified element information 340, theinterpretation module 304 generates, in accordance with interpretationrules 320 and a groupings list 334, an equation package to include oneor more of the identifiers of the tokenized words, a list of identifiersof the identigens of the equation package, a list of pairing identifiersfor sequential pairs of identigens, and a quality metric associated witheach sequential pair of identigens (e.g., likelihood of a properinterpretation). For instance, the interpretation module 304 produces afirst equation package that includes a first identigen pairing of ablack bat (e.g., flying bat with a higher quality metric level), thesecond pairing of bat eats (e.g., the flying bat eats, with a higherquality metric level), and a third pairing of eats fruit, and theinterpretation module 304 produces a second equation package thatincludes a first pairing of a black bat (e.g., baseball bat, with aneutral quality metric level), the second pairing of bat eats (e.g., thebaseball bat eats, with a lower quality metric level), and a thirdpairing of eats fruit.

A fourth step of the processing of the content to produce the knowledgeincludes selecting a surviving equation package associated with a mostfavorable confidence level. As depicted in FIG. 8F, a specific exampleof the fourth step includes the interpretation module 304 applyinginterpretation rules 320 (i.e., inference, pragmatic engine, utilizingthe identifiers of the identigens to match against known validcombinations of identifiers of entigens) to reduce a number ofpermutations of the sequential combinations of identigens to produceinterpreted information 344 that includes identification of at least oneequation package as a surviving interpretation SI (e.g., higher qualitymetric level).

Non-surviving equation packages are eliminated that compare unfavorablyto pairing rules and/or are associated with an unfavorable qualitymetric levels to produce a non-surviving interpretation NSI 2 (e.g.,lower quality metric level), where an overall quality metric level maybe assigned to each equation package based on quality metric levels ofeach pairing, such that a higher quality metric level of an equationpackage indicates a higher probability of a most favorableinterpretation. For instance, the interpretation module 304 eliminatesthe equation package that includes the second pairing indicating thatthe “baseball bat eats” which is inconsistent with a desired qualitymetric level of one or more of the groupings list 334 and theinterpretation rules 320 and selects the equation package associatedwith the “flying bat eats” which is favorably consistent with the one ormore of the quality metric levels of the groupings list 334 and theinterpretation rules 320.

A fifth step of the processing of the content to produce the knowledgeutilizing the confidence level includes integrating knowledge of thesurviving equation package into a knowledge base. For example,integrating at least a portion of the reduced OCA combinations into agraphical database to produce updated knowledge. As another example, theportion of the reduced OCA combinations may be translated into rows andcolumns entries when utilizing a rows and columns database rather than agraphical database. When utilizing the rows and columns approach for theknowledge base, subsequent access to the knowledge base may utilizestructured query language (SQL) queries.

As depicted in FIG. 8G, a specific example of the fifth step includesthe IEI control module 308 recovering fact base information 600 from SSmemory 96 to identify a portion of the knowledge base for potentialmodification utilizing the OCAs of the surviving interpretation SI 1(i.e., compare a pattern of relationships between the OCAs of thesurviving interpretation SI 1 from the interpreted information 344 torelationships of OCAs of the portion of the knowledge base includingpotentially new quality metric levels).

The fifth step further includes determining modifications (e.g.,additions, subtractions, further clarifications required wheninformation is complex, etc.) to the portion of the knowledge base basedon the new quality metric levels. For instance, the IEI control module308 causes adding the element “black” as a “describes” relationship ofan existing bat OCA and adding the element “fruit” as an eats “does to”relationship to implement the modifications to the portion of the factbase information 600 to produce updated fact base information 608 forstorage in the SS memory 96.

FIG. 8H is a logic diagram of an embodiment of a method for processingcontent to produce knowledge for storage within a computing system. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-8E,8F, and also FIG. 8G. The method includes step 650 where a processingmodule of one or more processing modules of one or more computingdevices of the computing system identifies words of an ingested phraseto produce tokenized words. The identified includes comparing words toknown words of dictionary entries to produce identifiers of known words.

For each tokenized word, the method continues at step 651 where theprocessing module identifies one or more identigens that corresponds tothe tokenized word, where each identigen describes one of an object, acharacteristic, and an action (e.g., OCA). The identifying includesperforming a lookup of identifiers of the one or more identigensassociated with each tokenized word, where he different identifiersassociated with each of the potential object, the characteristic, andthe action associated with the tokenized word.

The method continues at step 652 where the processing module, for eachpermutation of sequential combinations of identigens, generates aplurality of equation elements to form a corresponding equation package,where each equation element describes a relationship betweensequentially linked pairs of identigens, where each sequential linkingpairs a preceding identigen to a next identigen. For example, for eachpermutation of identigens of each tokenized word, the processing modulegenerates the equation package to include a plurality of equationelements, where each equation element describes the relationship (e.g.,describes, acts on, is a, belongs to, did, did too, etc.) betweensequentially adjacent identigens of a plurality of sequentialcombinations of identigens. Each equation element may be furtherassociated with a quality metric to evaluate a favorability level of aninterpretation in light of the sequence of identigens of the equationpackage.

The method continues at step 653 where the processing module selects asurviving equation package associated with most favorableinterpretation. For example, the processing module applies rules (i.e.,inference, pragmatic engine, utilizing the identifiers of the identigensto match against known valid combinations of identifiers of entigens),to reduce the number of permutations of the sequential combinations ofidentigens to identify at least one equation package, wherenon-surviving equation packages are eliminated the compare unfavorablyto pairing rules and/or are associated with an unfavorable qualitymetric levels to produce a non-surviving interpretation, where anoverall quality metric level is assigned to each equation package basedon quality metric levels of each pairing, such that a higher qualitymetric level indicates an equation package with a higher probability offavorability of correctness.

The method continues at step 654 where the processing module integratesknowledge of the surviving equation package into a knowledge base. Forexample, the processing module integrates at least a portion of thereduced OCA combinations into a graphical database to produce updatedknowledge. The integrating may include recovering fact base informationfrom storage of the knowledge base to identify a portion of theknowledge base for potential modifications utilizing the OCAs of thesurviving equation package (i.e., compare a pattern of relationshipsbetween the OCAs of the surviving equation package to relationships ofthe OCAs of the portion of the knowledge base including potentially newquality metric levels). The integrating further includes determiningmodifications (e.g., additions, subtractions, further clarificationsrequired when complex information is presented, etc.) to produce theupdated knowledge base that is based on fit of acceptable quality metriclevels, and implementing the modifications to the portion of the factbase information to produce the updated fact base information forstorage in the portion of the knowledge base.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 8J and 8K are schematic block diagrams of another embodiment of acomputing system that includes the content ingestion module 300 of FIG.5E, the element identification module 302 of FIG. 5E, the interpretationmodule 304 of FIG. 5E, the answer resolution module 306 of FIG. 5E, andthe SS memory 96 of FIG. 2. Generally, an embodiment of this inventionprovides solutions where the computing system 10 supports for generatinga query response to a query utilizing a knowledge base.

The generating of the query response to the query includes a series ofsteps. For example, a first step includes identifying words of aningested query to produce tokenized words. As depicted in FIG. 8J, aspecific example of the first step includes the content ingestion module300 comparing words of query info 138 to dictionary entries to produceformatted content 314 that includes identifiers of known words. Forinstance, the content ingestion module 300 produces identifiers for eachword of the query “what black animal flies and eats fruit and insects?”

A second step of the generating of the query response to the queryincludes, for each tokenized word, identifying one or more identigensthat correspond the tokenized word, where each identigen describes oneof an object, a characteristic, and an action (OCA). As depicted in FIG.8J, a specific example of the second step includes the elementidentification module 302 performing a look up of identifiers, utilizingan element list 332 and in accordance with element rules 318, of the oneor more identigens associated with each tokenized word of the formattedcontent 314 to produce identified element information 340. A uniqueidentifier is associated with each of the potential object, thecharacteristic, and the action associated with a particular tokenizedword. For instance, the element identification module 302 produces asingle identigen identifier for each of the black color, an animal,flies, eats, fruit, and insects.

A third step of the generating of the query response to the queryincludes, for each permutation of sequential combinations of identigens,generating a corresponding equation package (i.e., candidateinterpretation). The equation package includes a sequential linking ofpairs of identigens, where each sequential linking pairs a precedingidentigen to a next identigen. An equation element describes arelationship between paired identigens (OCAs) such as describes, actson, is a, belongs to, did, did to, etc.

As depicted in FIG. 8J, a specific example of the third step includesthe interpretation module 304, for each permutation of identigens ofeach tokenized word of the identified element information 340,generating the equation packages in accordance with interpretation rules320 and a groupings list 334 to produce a series of equation elementsthat include pairings of identigens. For instance, the interpretationmodule 304 generates a first pairing to describe a black animal, asecond pairing to describe an animal that flies, a third pairing todescribe flies and eats, a fourth pairing to describe eats fruit, and afifth pairing to describe eats fruit and insects.

A fourth step of the generating the query response to the query includesselecting a surviving equation package associated with a most favorableinterpretation. As depicted in FIG. 8J, a specific example of the fourthstep includes the interpretation module 304 applying the interpretationrules 320 (i.e., inference, pragmatic engine, utilizing the identifiersof the identigens to match against known valid combinations ofidentifiers of entigens) to reduce the number of permutations of thesequential combinations of identigens to produce interpreted information344. The interpreted information 344 includes identification of at leastone equation package as a surviving interpretation SI 10, wherenon-surviving equation packages, if any, are eliminated that compareunfavorably to pairing rules to produce a non-surviving interpretation.

A fifth step of the generating the query response to the query includesutilizing a knowledge base, generating a query response to the survivingequation package of the query, where the surviving equation package ofthe query is transformed to produce query knowledge for comparison to aportion of the knowledge base. An answer is extracted from the portionof the knowledge base to produce the query response.

As depicted in FIG. 8K, a specific example of the fifth step includesthe answer resolution module 306 interpreting the survivinginterpretation SI 10 of the interpreted information 344 in accordancewith answer rules 322 to produce query knowledge QK 10 (i.e., agraphical representation of knowledge when the knowledge base utilizes agraphical database). For example, the answer resolution module 306accesses fact base information 600 from the SS memory 96 to identify theportion of the knowledge base associated with a favorable comparison ofthe query knowledge QK 10 (e.g., by comparing attributes of the queryknowledge QK 10 to attributes of the fact base information 600), andgenerates preliminary answers 354 that includes the answer to the query.For instance, the answer is “bat” when the associated OCAs of bat, suchas black, eats fruit, eats insects, is an animal, and flies, aligns withOCAs of the query knowledge.

FIG. 8L is a logic diagram of an embodiment of a method for generating aquery response to a query utilizing knowledge within a knowledge basewithin a computing system. In particular, a method is presented for usein conjunction with one or more functions and features described inconjunction with FIGS. 1-8D, 8J, and also FIG. 8K. The method includesstep 655 where a processing module of one or more processing modules ofone or more computing devices of the computing system identifies wordsof an ingested query to produce tokenized words. For example, theprocessing module compares words to known words of dictionary entries toproduce identifiers of known words.

For each tokenized word, the method continues at step 656 where theprocessing module identifies one or more identigens that correspond tothe tokenized word, where each identigen describes one of an object, acharacteristic, and an action. For example, the processing moduleperforms a lookup of identifiers of the one or more identigensassociated with each tokenized word, where different identifiersassociated with each permutation of a potential object, characteristic,and action associated with the tokenized word.

For each permutation of sequential combinations of identigens, themethod continues at step 657 where the processing module generates aplurality of equation elements to form a corresponding equation package,where each equation element describes a relationship betweensequentially linked pairs of identigens. Each sequential linking pairs apreceding identigen to a next identigen. For example, for eachpermutation of identigens of each tokenized word, the processing moduleincludes all other permutations of all other tokenized words to generatethe equation packages. Each equation package includes a plurality ofequation elements describing the relationships between sequentiallyadjacent identigens of a plurality of sequential combinations ofidentigens.

The method continues at step 658 where the processing module selects asurviving equation package associated with a most favorableinterpretation. For example, the processing module applies rules (i.e.,inference, pragmatic engine, utilizing the identifiers of the identigensto match against known valid combinations of identifiers of entigens) toreduce the number of permutations of the sequential combinations ofidentigens to identify at least one equation package. Non-survivingequation packages are eliminated the compare unfavorably to pairingrules.

The method continues at step 659 where the processing module generates aquery response to the surviving equation package, where the survivingequation package is transformed to produce query knowledge for locatingthe portion of a knowledge base that includes an answer to the query. Asan example of generating the query response, the processing moduleinterprets the surviving the equation package in accordance with answerrules to produce the query knowledge (e.g., a graphical representationof knowledge when the knowledge base utilizes a graphical databaseformat).

The processing module accesses fact base information from the knowledgebase to identify the portion of the knowledge base associated with afavorable comparison of the query knowledge (e.g., favorable comparisonof attributes of the query knowledge to the portion of the knowledgebase, aligning favorably comparing entigens without conflictingentigens). The processing module extracts an answer from the portion ofthe knowledge base to produce the query response.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 9A is a schematic block diagram of another embodiment of acomputing system that includes consumer content sources 660, consumertransaction information sources 662, and the artificial intelligence(AI) server 20-1 of FIG. 1. The consumer transaction information sources662 includes the transactional servers 18-1 through 18-N. The consumercontent sources 660 includes the content sources 16-1 through 16-N ofFIG. 1. In particular, content sources associated with the consumercontent sources 660 provides one or more of social media information,newsfeeds, user activities, user location information, user scheduleinformation, etc. and the transactional servers associated with theconsumer transaction information sources 662 provide one or more ofgroup and/or individual consumer purchasing history, transactioninformation, product availability information, product descriptioninformation, historical product performance information, product pricinginformation, etc. The AI server 20-1 includes the processing module 50-1of FIG. 2 and the solid state (SS) memory 96 of FIG. 2. The processingmodule 50-1 includes the collections module 120 of FIG. 4A, theidentigen entigen intelligence (IEI) module 122 of FIG. 4A, and thequery module 124 of FIG. 4A. Generally, an embodiment of this inventionpresents solutions where the computing system functions to produce aresponse to a query with regards to likelihood to purchase.

In an example of operation of the responding to the query, the querymodule 124 interprets a received query request 136 (e.g., from thetransactional server 18-1) to produce query requirements. Theinterpreting includes one or more of determining content requirements,determining source requirements, determining answer timing requirements,and identifying at least one domain associated with the query request136. For example, the query module 124 determines the contentrequirements to include a query with regards to what products that aperson is likely to purchase next, determines the source requirements toinclude the consumer content sources 660 and the consumer transactioninformation sources 662, determines the answer timing requirements toinclude a two hour time frame, and identifies consumer likelihood topurchase as the domain when receiving the query request 136 thatincludes a question “what products are [person(s)] likely to purchasenext?”

Having produced the query requirements, the query module 124 issues atleast one of an IEI request 244 and a collections request 132 based onthe query request 136. For example, the query module 124 generates theIEI request 244 and sends the IEI request 244 to the IEI module 122 whenthe source requirements suggest that the IEI module 122 is able toprovide an immediate response. As another example, the query module 124generates the collections request 132 and sends the collections request132 to the collections module 120 when the source requirements suggestthat a future time frame is associated with the query request 136 andmore content is required. For instance, the query module 124 issues thecollections request 132 to the collections module 120 to facilitatecollecting content over the next two hours and subsequently issues theIEI request 244 to the IEI module 122 to generate the response to thequery.

When receiving the IEI request 244, the IEI module 122 formats the IEIrequest 244 to produce human expressions that include question contentand question information. The formatting includes analyzing the IEIrequest 244 for recognizable human expressions of question content andquestion information in accordance with rules and fact base information600 (e.g., facts pertaining to likelihood to purchase) obtained from theSS memory 96.

Having produced the human expressions, the IEI module 122 applies “IEIprocessing” to the human expressions to produce one or more of newknowledge, a preliminary answer, and an answer quality level associatedwith the preliminary answer. The IEI processing includes identifyingpermutations of identigens, reducing the permutations in accordance withthe rules, mapping the reduced permutations of identigens to entigens togenerate knowledge, processing the knowledge in accordance with the factbase (e.g., fact base info 600) to produce the preliminary answer (e.g.,likelihood of purchase of a particular product by a particular person),and generating the answer quality level based on the preliminary answerand the request (e.g., the IEI request 244, the query request 136).

When the answer quality level is unfavorable, the IEI module 122 issuesa collections request 132 to the collections module 120 to gather morecontent to produce knowledge to enable a desired favorable quality levelof the answer. The issuing includes generating the collections request132 based on one or more of the IEI requests 244, the preliminaryanswer, elements of the fact base information 600 (e.g., the presentknowledge base), and the answer quality level.

The collections module 120 interprets one or more collections requests132 to produce content requirements. The interpreting includes one ormore of determining content selection requirements, determining sourceselection requirements, and determining content acquisition timingrequirements. For example, the collections module 120 determines thesource selection requirements to include selecting the content sources16-1 through 16-N of the consumer content sources 660 and to includeselecting the transactional servers 18-1 through 18-N of the consumertransaction information sources 662, determines the content selectionrequirements to include content associated with the likelihood topurchase, and determines the content acquisition timing requirements toinclude a two hour time span.

Having produced the content requirements, the collections module 120issues a plurality of content requests 126 to a plurality of contentsources identified by the content requirements (e.g., to the contentsources 16-1 through 16-N) and issues one or more transactioninformation requests 664 to the consumer transaction information sources662. For example, the collections module 120 identifies the plurality ofconsumer content sources, generates the content requests 126 based onthe content requirements, and sends the plurality of content requests on26 to the identified plurality of content sources 16-1 through 16-N. Asanother example, the collection module 120 identifies one or moretransactional servers of the consumer transaction information sources662 based on the content requirements (e.g., historical consumerpurchasing history, availability of the consumer to make a purchasewithin the next two hours, likely needs of the consumer within the nexttwo hours), generates the one or more transaction information requests664, and sends the one or more transaction information request 664 tothe identified one or more transactional servers of the consumertransaction information sources 662.

Having issued the plurality of content requests 126 and the one or moretransaction information request 664, the collections module 120interprets a plurality of content responses 128 and one or moretransaction information responses 666 to determine whether a responsequality level is favorable. The interpreting includes analyzing theplurality of content responses 128 and the one or more transactioninformation responses 666 to produce an estimated response qualitylevel, and indicating a favorable response quality level when theestimated response quality level compares favorably to a minimumresponse quality threshold level (e.g., greater than). When the responsequality level is favorable, the collections module 120 issues acollections response 134 to the IEI module 122, where the collectionsresponse 134 includes further content. For example, the collectionsmodule 120 generates the collections response 134 to include the furthercontent and the estimated response quality level, and sends thecollections response 134 to the IEI module 122.

The IEI module 122 analyzes the further content based on one or more ofthe IEI request 244 and the fact base information 600 to produce one ormore of updated fact base information (e.g., new knowledge for storagein the SS memory 96) and a preliminary answer with an associatedpreliminary answer quality level. For example, the IEI module 122reasons the further content with the fact base information 600 toproduce the preliminary answer which identifies the consumer likelihoodto purchase. When the answer quality level is favorable, the IEI module122 issues an IEI response 246 to the query module 124 where the IEIresponse 246 includes the preliminary answer associated with a favorableanswer quality level. The query module 124 interprets the receivedanswer to produce a quality level of the received answer. For example,the query module 124 analyzes the preliminary answer in accordance withthe query requirements and the rules to generate the quality level ofthe received answer. When the quality level of the received answer isfavorable, the query module 124 issues a query response 140 to thetransactional server 18-1, where the query response 140 includes theanswer associated with the favorable quality level of the answer.

FIG. 9B is a data flow diagram for answering questions utilizingaccumulated knowledge within a computing system. The data flow diagramincludes the IEI module 122 of FIG. 9A and fact base information 600 inthe form of content sources 670 and transaction sources 672. The contentsources 670 includes a plurality of source C1-CN groupings table 674 andthe transaction sources 672 includes a plurality of source T1-TNgroupings table 676. Each groupings table 674 and 676 includes multiplefields including fields for a group (GRP) identifier (ID) 586, wordstrings 588, identigen (IDN) string 626, and an entigen (ENI) 628. Forinstance, the groupings tables 674 of the content sources 670 includesword strings and identifiers associated with consumer content, such asthe a consumer has a preference for product A, the product need in onehour is high, and the consumer location is at L1. As another instance,the groupings tables 676 of the transaction sources 672 includespurchase propensity lowest for product A, purchase propensity is lowwhen needed is low, and purchase propensity is high when the consumer isat location L1.

As an example of operation of providing an answer to a query, the IEImodule 122 interprets the IEI request 244, facilitates obtaining thefact base information 600, and generates the preliminary answer based onthe rules 316 and associated time frames relevant to the question of theIEI request 244. For example, the IEI module 122 generates thepreliminary answer to indicate that “best purchase propensity now is forproduct B”. For instance, the IEI module 122 identifies the preferencefor product B, the product need is highest in one hour, the consumeractivity is A1, the purchase propensity is highest when the consumer isengaged in the activity A1, the purchase propensity is highest when theconsumer is at location L1, and the consumer location is location L1.

FIG. 9C is a logic diagram of an embodiment of a method for producing aresponse to a query within a computing system. In particular, a methodis presented for use in conjunction with one or more functions andfeatures described in conjunction with FIGS. 1-8L, 9A-9B, and also FIG.9C. The method includes step 680 where a processing module of one ormore processing modules of one or more computing devices of thecomputing system interprets a received query request from a requester toproduce query requirements. The interpreting includes one or more ofdetermining content requirements, (e.g., to determine propensity topurchase), determining source requirements, determining answer timingrequirements, and identifying a domain (e.g., consumer purchasing)associated with the query request.

The method continues at step 682 where the processing module IEIprocesses human expressions of the received query request based on afact base generated from previous content to produce a preliminaryanswer. The processing may include formatting portions of the queryrequest in accordance with formatting rules to produce recognizablehuman expressions of content and question information. For example, theprocessing module produces the question information to include a requestto determine consumer purchase likelihood for a particular domain (e.g.,purchasing propensity). The processing may further include identifyingpermutations of identigens within the human expressions, reducing thepermutations, mapping the reduce permutations to entigens to produceknowledge, processing the knowledge in accordance with a fact base toproduce the preliminary answer, and generating an answer quality levelassociated with the preliminary answer. For instance, the processingmodule generates a relatively low answer quality level when the questionrelates to gathering information over a subsequent two hours such thatmore content must be gathered to produce an answer associated with ahigher and more favorable answer quality level.

When the answer quality level is unfavorable, the method continues atstep 684 where the processing module generates content requirements. Thegenerating of the content requirements includes determining, based onone or more of the query requirements, preliminary answer, and theanswer quality level, one or more of content selection requirements,source selection requirements, and acquisition timing requirements.

The method continues at step 686 for the processing module obtainsfurther content from a plurality of sources based on the contentrequirements. For example, the processing module identifies theplurality of sources (e.g., consumer content sources, consumertransaction information sources), generates requests based on thecontent requirements, and sends the plurality of content requests to theplurality of identified content sources, analyzes a plurality of contentresponses to produce an estimated quality level, indicates favorablequality level when the estimated quality level compares favorably to aminimum quality threshold level, and indicates unfavorable quality levelto facilitate collective more content when the estimated quality levelcompares unfavorably to the minimum quality threshold level.

The method continues at step 688 where the processing module IEIprocesses human expressions of the further content based on the factbase to produce an updated preliminary answer that includes a propensityto purchase. For example, the processing module analyzes, based on oneor more of the query request, the fact base info associated with theidentified domain, and the further content to produce one or more ofupdated fact base info (e.g., new knowledge), the updated preliminaryanswer (e.g., updated consumer purchase history and general consumerinformation), and an associated answer quality level. The analyzing mayinclude reasoning the further content with the fact base to produce theupdated fact base info and the preliminary answer to include thepurchasing propensity.

When the updated answer quality level is favorable, the method continuesat step 690 where the processing module issues a query response to therequest are that identifies the propensity to purchase. The issuingincludes one or more of analyzing the preliminary answers in accordancewith the query requirements and the rules to generate the updatedquality level, generating the query response to include the answerassociated with favorable quality level, and sending the query responseto the requester

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 10A is a schematic block diagram of another embodiment of acomputing system that includes attack content sources 700, theartificial intelligence (AI) server 20-1 of FIG. 1, and the user device12-1 of FIG. 1. The attack content sources 700 includes the contentsources 16-1 through 16-N of FIG. 1. In particular, content sourcesassociated with attack content provide one or more of Internet traffic,Internet traffic summaries, people information (e.g., medical records,court records, school records, police reports, terrorist watchlist, gunregistration lists, group affiliations, etc.), physical world data(environmental, structural, machine, etc.), community beliefs (e.g.,social media), and news outlet information (e.g., press releases,periodicals, radio broadcast, television news, financial market news,etc.), etc. The AI server 20-1 includes the processing module 50-1 ofFIG. 2 and the solid state (SS) memory 96 of FIG. 2. The processingmodule 50-1 includes the collections module 120 of FIG. 4A, theidentigen entigen intelligence (IEI) module 122 of FIG. 4A, and thequery module 124 of FIG. 4A. Generally, an embodiment of this inventionpresents solutions where the computing system functions to produce aresponse to a query regarding likelihood of an attack (e.g., of aphysical event, a cyber-attack, a physical attack, a political attack,etc.) based on factual interpretations of early stages of the attackand/or likely distractions of a pre-attack sequence.

In an example of operation of the responding to the query, the querymodule 124 interprets a received query request 136 to produce queryrequirements. The interpreting includes one or more of determiningcontent requirements, determining source requirements, determininganswer timing requirements, and identifying at least one domainassociated with the query request 136. For example, the query module 124determines the content requirements to include facts that can lead toprediction of the attack, determines the source requirements to includethe attack content sources 700, determines the answer timingrequirements to include a timeframe associated with the predictedoccurrence, and identifies a particular type of attack (e.g., cyber) asthe domain when receiving the query request 136 that includes a question“what is a temporal view of likelihood of an attack occurring.”

Having produced the query requirements, the query module 124 issues atleast one of an IEI request 244 and a collections request 132 based onthe query request 136. For example, the query module 124 generates theIEI request 244 and sends the IEI request 244 to the IEI module 122 whenthe source requirements suggest that the IEI module 122 is able toprovide an immediate response. As another example, the query module 124generates the collections request 132 and sends the collections request132 to the collections module 120 when the source requirements suggestthat a future time frame is associated with the query request 136 andmore content is required. For instance, the query module 124 issues thecollections request 132 to the collections module 120 to facilitatecollecting content over the next 20 minutes associated with a typicalpre-attack distraction of the query request 136 and subsequently issuesthe IEI request 244 to the IEI module 122 to generate the response tothe query.

When receiving the IEI request 244, the IEI module 122 formats the IEIrequest 244 to produce human expressions that include question contentand question information. The formatting includes analyzing the IEIrequest 244 for recognizable human expressions of question content andquestion information in accordance with rules and fact base information600 (e.g., facts pertaining to the attack) obtained from the SS memory96.

Having produced the human expressions, the IEI module 122 applies “IEIprocessing” to the human expressions to produce one or more of newknowledge, a preliminary answer, and an answer quality level associatedwith the preliminary answer. The IEI processing includes identifyingpermutations of identigens, reducing the permutations in accordance withthe rules, mapping the reduced permutations of identigens to entigens togenerate knowledge, processing the knowledge in accordance with the factbase (e.g., fact base info 600) to produce the preliminary answer, andgenerating the answer quality level based on the preliminary answer andthe request (e.g., the IEI request 244, the query request 136).

When the answer quality level is unfavorable, the IEI module 122 issuesa collections request 132 to the collections module 120 to gather morecontent to produce knowledge to enable a desired favorable quality levelof the answer. The issuing includes generating the collections request132 based on one or more of the IEI requests 244, the preliminaryanswer, elements of the fact base information 600 (e.g., the presentknowledge base), and the answer quality level.

The collections module 120 interprets one or more collections requests132 to produce content requirements. The interpreting includes one ormore of determining content selection requirements, determining sourceselection requirements, and determining content acquisition timingrequirements. For example, the collections module 120 determines thesource selection requirements to include selecting the content sources16-1 through 16-N of the attack content sources 700, determines thecontent selection requirements to include content associated with theattack (e.g., scenarios that are affiliated with the pre-attackdistractions and/or the attack), and determines the content acquisitiontiming requirements to include a time span for collection if any.

Having produced the content requirements, the collections module 120issues a plurality of content requests 126 to a plurality of contentsources identified by the content requirements (e.g., to the contentsources 16-1 through 16-N). For example, the collections module 120identifies the plurality of content sources, generates the contentrequests based on the content requirements, and sends the plurality ofcontent requests 126 to the identified plurality of content sources.

Having issued the plurality of content requests 126, the collectionsmodule 120 interprets a plurality of content responses 128 to determinewhether a response quality level is favorable.

The interpreting includes analyzing the plurality of content responses128 to produce an estimated response quality level, and indicating afavorable response quality level when the estimated response qualitylevel compares favorably to a minimum response quality threshold level(e.g., greater than). When the response quality level is favorable, thecollections module 120 issues a collections response 134 to the IEImodule 122, where the collections response 134 includes further content.For example, the collections module 120 generates the collectionsresponse 134 to include the further content and the estimated responsequality level, and sends the collections response 134 to the WI module122.

The LEI module 122 analyzes the further content based on one or more ofthe IEI request 244 and the fact base information 600 to produce one ormore of updated fact base information (e.g., new knowledge for storagein the SS memory 96) and a preliminary answer with an associatedpreliminary answer quality level. For example, the IEI module 122reasons the further content with the fact base information 600 toproduce the preliminary answer which predicts the likelihood of theattack. When the answer quality level is favorable, the IEI module 122issues an IEI response 246 to the query module 124 where the IEIresponse 246 includes the preliminary answer associated with a favorableanswer quality level. The query module 124 interprets the receivedanswer to produce a quality level of the received answer. For example,the query module 124 analyzes the preliminary answer in accordance withthe query requirements and the rules to generate the quality level ofthe received answer. When the quality level of the received answer isfavorable, the query module 124 issues a query response 140 to the userdevice 12-1, where the query response 140 includes the answer associatedwith the favorable quality level of the answer.

FIG. 10B is a data flow diagram for predicting an attack utilizingpre-attack sequence detection within a computing system, where acomputing device of the computing system performs the resolve answerstep 644, based on rules 316, time 702, and fact base info 600, oncontent that includes an estimated value and desired range for each of nconditions for each N sequences to produce preliminary answers 354. Eachcondition of the content describes status of an outside force that canbe determined based on fact base info 600 (e.g., a sign of a cyberattack, etc.). The computing device compares the estimated value of thecondition to a desired range (e.g., minimum/maximum of a metric)associated with the condition to produce the status (e.g., probabilityof a factual element based on the comparison. Each sequence includes anordered series of conditions that are estimated to have values thatcompare favorably to an associated desired value range to complete thesequence (e.g., ordering may be strict or flexible). The plurality ofsequences may include any number of sequences to link to the occurrence.

In an example of operation, one sequence is utilized with threeconditions to provide a likelihood of a physical attack on a nuclearplant, where the first condition is an Internet capture phraseindicating an issue with regards to the nuclear plant, the secondcondition is a more direct phrase captured on the Internet with regardsto a potential demise of the nuclear plant, and a third condition isevidence of an individual associated with the phrase captures to bewithin a threshold geographic proximity of the nuclear plant. Thecomputing device obtains the content for the first through thirdconditions and generates a preliminary answer 354 that indicates thatthe likelihood of an attack is elevated.

FIG. 10C is a logic diagram of an embodiment of a method for predictingan attack within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-8L, 10A-10B, and also FIG. 10C.The method includes step 720 where a processing module of one or moreprocessing modules of one or more computing devices of the computingsystem interprets a received query request from a requester to producequery requirements with regards to an attack. The interpreting includesone or more of determining content requirements, (e.g., to gatherconditions of sequences), determining source requirements, determininganswer timing requirements, and identifying a domain associated with thequery request (e.g., physical attack, cyber attack).

The method continues at step 722 where the processing module WIprocesses human expressions of the received query request based on afact base generated from previous content to produce a preliminaryattack answer. The processing may include formatting portions of thequery request in accordance with formatting rules to producerecognizable human expressions of content and question information. Forexample, the processing module produces the question information toinclude a request to determine likelihood of the attack (e.g.,identifying conditions and scenarios that lead to the attack).

The processing further includes identifying permutations of identigenswithin the human expressions, reducing the permutations, mapping thereduce permutations to entigens to produce knowledge, processing theknowledge in accordance with a fact base to produce the preliminaryanswer, and generating an answer quality level associated with thepreliminary answer. For instance, the processing module generates arelatively low answer quality level when the question relates togathering information over a subsequent time frame such that morecontent must be gathered to produce an answer associated with a higherand more favorable answer quality level (e.g., start looking for valuesof conditions associated with scenarios to support answering thelikelihood of attack question).

When the answer quality level is unfavorable, the method continues atstep 724 where the processing module generates content requirements. Thegenerating of the content requirements includes determining, based onone or more of the query requirements, preliminary answer, and theanswer quality level, one or more of content selection requirements,source selection requirements, and acquisition timing requirements.

The method continues at step 726 where the processing module obtainsfurther content from a plurality of attack content sources based on thecontent requirements. For example, the processing module identifies theplurality of content sources, generates content requests based on thecontent requirements, and sends the plurality of content requests to theplurality of identified attack content sources, analyzes a plurality ofcontent responses to produce an estimated quality level, indicatesfavorable quality level when the estimated quality level comparesfavorably to a minimum quality threshold level, and indicatesunfavorable quality level to facilitate collecting more content when theestimated quality level compares unfavorably to the minimum qualitythreshold level.

The method continues at step 728 where the processing module IEIprocesses human expressions of the further content based on the factbase to produce an updated preliminary attack answer that identifies thelikelihood of the attack. For example, the processing module analyzes,based on one or more of the query request, the fact base info associatedwith the identified domain, and the further content to produce one ormore of updated fact base info (e.g., new knowledge), the updatedpreliminary occurrence answer (e.g., likelihood of attack), and anassociated answer quality level. The analyzing may include reasoning thefurther content with the fact base to produce the updated fact base infoand the preliminary answer to include the likelihood of the attack.

When the updated answer quality level is favorable, the method continuesat step 730 where the processing module issues a query response to therequest are that predicts the likelihood of the attack. The issuingincludes one or more of analyzing the preliminary answers in accordancewith the query requirements and the rules to generate the updatedquality level, generating the query response to include the answerassociated with favorable quality level, and sending the query responseto the requester

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 11A is a schematic block diagram of another embodiment of acomputing system that includes product performance content sources 740,the transactional server 18-1 of FIG. 1, and the artificial intelligence(AI) server 20-1 of FIG. 1. The product performance content sources 740includes the content sources 16-1 through 16-N of FIG. 1. In particular,the content sources 16-1 through 16-N provides one or more of socialmedia information, user activities, user location information, userschedule information, user product comments, use time of products,Internet of things product data, product warranty information, etc. TheAI server 20-1 includes the processing module 50-1 of FIG. 2 and thesolid state (SS) memory 96 of FIG. 2. The processing module 50-1includes the collections module 120 of FIG. 4A, the identigen entigenintelligence (IEI) module 122 of FIG. 4A, and the query module 124 ofFIG. 4A. Generally, an embodiment of this invention presents solutionswhere the computing system functions to produce a response to a querywith regards to perceptions of product performance.

In an example of operation of the responding to the query, the querymodule 124 interprets a received query request 136 (e.g., from thetransactional server 18-1) to produce query requirements. Theinterpreting includes one or more of determining content requirements,determining source requirements, determining answer timing requirements,and identifying at least one domain associated with the query request136. For example, the query module 124 determines the contentrequirements to include a query with regards to perceptions of productperformance, determines the source requirements to include the productperformance content sources 740, determines the answer timingrequirements to include a two week time frame, and identifies productperformance perceptions as the domain when receiving the query request136 that includes a question “what is affecting product churn for[product(s)]?”

Having produced the query requirements, the query module 124 issues atleast one of an IEI request 244 and a collections request 132 based onthe query request 136. For example, the query module 124 generates theIEI request 244 and sends the IEI request 244 to the IEI module 122 whenthe source requirements suggest that the IEI module 122 is able toprovide an immediate response. As another example, the query module 124generates the collections request 132 and sends the collections request132 to the collections module 120 when the source requirements suggestthat a future time frame is associated with the query request 136 andmore content is required. For instance, the query module 124 issues thecollections request 132 to the collections module 120 to facilitatecollecting content over the next two weeks and subsequently issues theIEI request 244 to the LEI module 122 to generate the response to thequery.

When receiving the LEI request 244, the LEI module 122 formats the LEIrequest 244 to produce human expressions that include question contentand question information. The formatting includes analyzing the LEIrequest 244 for recognizable human expressions of question content andquestion information in accordance with rules and fact base information600 (e.g., facts pertaining to perceptions of product performance)obtained from the SS memory 96.

Having produced the human expressions, the LEI module 122 applies “IEIprocessing” to the human expressions to produce one or more of newknowledge, a preliminary answer, and an answer quality level associatedwith the preliminary answer. The LEI processing includes identifyingpermutations of identigens, reducing the permutations in accordance withthe rules, mapping the reduced permutations of identigens to entigens togenerate knowledge, processing the knowledge in accordance with the factbase (e.g., fact base info 600) to produce the preliminary answer (e.g.,perceptions of product performance), and generating the answer qualitylevel based on the preliminary answer and the request (e.g., the LEIrequest 244, the query request 136).

When the answer quality level is unfavorable, the LEI module 122 issuesa collections request 132 to the collections module 120 to gather morecontent to produce knowledge to enable a desired favorable quality levelof the answer. The issuing includes generating the collections request132 based on one or more of the LEI requests 244, the preliminaryanswer, elements of the fact base information 600 (e.g., the presentknowledge base), and the answer quality level.

The collections module 120 interprets one or more collections requests132 to produce content requirements. The interpreting includes one ormore of determining content selection requirements, determining sourceselection requirements, and determining content acquisition timingrequirements. For example, the collections module 120 determines thesource selection requirements to include selecting the content sources16-1 through 16-N of the product performance content sources 740,determines the content selection requirements to include contentassociated with the likelihood to purchase, and determines the contentacquisition timing requirements to include a two week time span.

Having produced the content requirements, the collections module 120issues a plurality of content requests 126 to a plurality of contentsources identified by the content requirements (e.g., to the contentsources 16-1 through 16-N). For example, the collections module 120identifies the plurality of product performance content sources,generates the content requests 126 based on the content requirements,and sends the plurality of content requests 126 to the identifiedplurality of content sources 16-1 through 16-N.

Having issued the plurality of content requests 126, the collectionsmodule 120 interprets a plurality of content responses 128 to determinewhether a response quality level is favorable. The interpreting includesanalyzing the plurality of content responses 128 to produce an estimatedresponse quality level, and indicating a favorable response qualitylevel when the estimated response quality level compares favorably to aminimum response quality threshold level (e.g., greater than). When theresponse quality level is favorable, the collections module 120 issues acollections response 134 to the IEI module 122, where the collectionsresponse 134 includes further content. For example, the collectionsmodule 120 generates the collections response 134 to include the furthercontent and the estimated response quality level, and sends thecollections response 134 to the IEI module 122.

The IEI module 122 analyzes the further content based on one or more ofthe IEI request 244 and the fact base information 600 to produce one ormore of updated fact base information (e.g., new knowledge for storagein the SS memory 96) and a preliminary answer with an associatedpreliminary answer quality level. For example, the IEI module 122reasons the further content with the fact base information 600 toproduce the preliminary answer which identifies the perceptions ofproduct performance. When the answer quality level is favorable, the IEImodule 122 issues an IEI response 246 to the query module 124 where theIEI response 246 includes the preliminary answer associated with afavorable answer quality level. The query module 124 interprets thereceived answer to produce a quality level of the received answer. Forexample, the query module 124 analyzes the preliminary answer inaccordance with the query requirements and the rules to generate thequality level of the received answer. When the quality level of thereceived answer is favorable, the query module 124 issues a queryresponse 140 to the transactional server 18-1, where the query response140 includes the answer associated with the favorable quality level ofthe answer.

FIG. 11B is a data flow diagram for providing an answer to a questionwithin a computing system. The data flow diagram includes the IEI module122 of FIG. 11A and fact base information 600 in the form of contentsources 750. The content sources 750 includes a plurality of sourceP1-PN groupings table 752. Each groupings table 752 includes multiplefields including fields for a group (GRP) identifier (ID) 586, wordstrings 588, identigen (IDN) string 626, and an entigen (ENI) 628. Forinstance, the groupings tables 752 of the content sources 750 includesword strings and identifiers associated with consumer productperformance perceptions, such as product B is better than product A,product A needs feature X, and product A price is too high.

As an example of operation of providing an answer to a query, the IEImodule 122 interprets the IEI request 244, facilitates obtaining thefact base information 600, and generates the preliminary answer based onthe rules 316 and associated time frames relevant to the question of theIEI request 244. For example, the IEI module 122 generates thepreliminary answer to indicate that “unfavorable performance perceptionfor product A is 30% of consumers”. For instance, the IEI module 122identifies the preference for product B over product A, the product Aneeds feature X, and the product A price is too high.

FIG. 11C is a logic diagram of an embodiment of a method for providingan answer to a question within a computing system. In particular, amethod is presented for use in conjunction with one or more functionsand features described in conjunction with FIGS. 1-8L, 11A-11B, and alsoFIG. 11C. The method includes step 760 where a processing module of oneor more processing modules of one or more computing devices of thecomputing system interprets a received query request from a requester toproduce query requirements. The interpreting includes one or more ofdetermining content requirements, (e.g., to determine productperformance perceptions), determining source requirements, determininganswer timing requirements, and identifying a domain (e.g., consumerproduct performance perceptions) associated with the query request.

The method continues at step 762 where the processing module IEIprocesses human expressions of the received query request based on afact base generated from previous content to produce a preliminaryanswer with regards to product performance. The processing may includeformatting portions of the query request in accordance with formattingrules to produce recognizable human expressions of content and questioninformation. For example, the processing module produces the questioninformation to include a request to determine consumer productperformance perceptions for a particular domain (e.g., product churn).The processing may further include identifying permutations ofidentigens within the human expressions, reducing the permutations,mapping the reduced permutations to entigens to produce knowledge,processing the knowledge in accordance with a fact base to produce thepreliminary answer, and generating an answer quality level associatedwith the preliminary answer. For instance, the processing modulegenerates a relatively low answer quality level when the questionrelates to gathering information over a subsequent two weeks such thatmore content must be gathered to produce an answer associated with ahigher and more favorable answer quality level.

When the answer quality level is unfavorable, the method continues atstep 764 where the processing module generates content requirements. Thegenerating of the content requirements includes determining, based onone or more of the query requirements, preliminary answer, and theanswer quality level, one or more of content selection requirements,source selection requirements, and acquisition timing requirements.

The method continues at step 766 where the processing module obtainsfurther content from a plurality of sources based on the contentrequirements. For example, the processing module identifies theplurality of sources (e.g., product performance content sources),generates requests based on the content requirements, and sends theplurality of content requests to the plurality of identified contentsources, analyzes a plurality of content responses to produce anestimated quality level, indicates favorable quality level when theestimated quality level compares favorably to a minimum qualitythreshold level, and indicates unfavorable quality level to facilitatecollective more content when the estimated quality level comparesunfavorably to the minimum quality threshold level.

The method continues at step 768 where the processing module IEIprocesses human expressions of the further content based on the factbase to produce an updated preliminary answer that includes a perceptionof product performance. For example, the processing module analyzes,based on one or more of the query request, the fact base info associatedwith the identified domain, and the further content to produce one ormore of updated fact base info (e.g., new knowledge), the updatedpreliminary answer (e.g., updated consumer product performanceperceptions over the last two weeks and an associated answer qualitylevel. The analyzing may include reasoning the further content with thefact base to produce the updated fact base info and the preliminaryanswer to include the product performance perceptions.

When the updated answer quality level is favorable, the method continuesat step 770 where the processing module issues a query response to therequest are that identifies the propensity to purchase. The issuingincludes one or more of analyzing the preliminary answers in accordancewith the query requirements and the rules to generate the updatedquality level, generating the query response to include the answerassociated with favorable quality level, and sending the query responseto the requester

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 12A is a schematic block diagram of another embodiment of acomputing system that includes media sources 780, the user device 12-1of FIG. 1, and the artificial intelligence (AI) server 20-1 of FIG. 1.The media sources 780 includes the content sources 16-1 through 16-N ofFIG. 1. In particular, the content sources 16-1 through 16-N providesone or more of social media information, newsfeeds, press releases, bloginfo, periodicals, library info, records, etc. The AI server 20-1includes the processing module 50-1 of FIG. 2 and the solid state (SS)memory 96 of FIG. 2. The processing module 50-1 includes the collectionsmodule 120 of FIG. 4A, the identigen entigen intelligence (IEI) module122 of FIG. 4A, and the query module 124 of FIG. 4A. Generally, anembodiment of this invention presents solutions where the computingsystem functions to produce a response to a query with regards tofactual likelihood of a topic based on curated knowledge.

In an example of operation of the responding to the query, the querymodule 124 interprets a received query request 136 (e.g., from the userdevice 12-1) to produce query requirements. The interpreting includesone or more of determining content requirements, determining sourcerequirements, determining answer timing requirements, and identifying atleast one domain associated with the query request 136. For example, thequery module 124 determines the content requirements to include a querywith regards to factual likelihood of a topic, determines the sourcerequirements to include the media sources 780, determines the answertiming requirements to include one hour time frame, and identifiesfactual likelihood of a topic as the domain when receiving the queryrequest 136 that includes a question: “what is most likely factual about[topic]?”

Having produced the query requirements, the query module 124 issues atleast one of an IEI request 244 and a collections request 132 based onthe query request 136. For example, the query module 124 generates theLEI request 244 and sends the LEI request 244 to the IEI module 122 whenthe source requirements suggest that the IEI module 122 is able toprovide an immediate response. As another example, the query module 124generates the collections request 132 and sends the collections request132 to the collections module 120 when the source requirements suggestthat a future time frame is associated with the query request 136 andmore content is required. For instance, the query module 124 issues thecollections request 132 to the collections module 120 to facilitatecollecting content over the next hour and subsequently issues the IEIrequest 244 to the IEI module 122 to generate the response to the query.

When receiving the IEI request 244, the IEI module 122 formats the IEIrequest 244 to produce human expressions that include question contentand question information. The formatting includes analyzing the IEIrequest 244 for recognizable human expressions of question content andquestion information in accordance with rules and fact base information600 (e.g., factual likelihood of a topic) obtained from the SS memory96.

Having produced the human expressions, the IEI module 122 applies “IEIprocessing” to the human expressions to produce one or more of newknowledge, a preliminary answer, and an answer quality level associatedwith the preliminary answer. The IEI processing includes identifyingpermutations of identigens, reducing the permutations in accordance withthe rules, mapping the reduced permutations of identigens to entigens togenerate knowledge, processing the knowledge in accordance with the factbase (e.g., fact base info 600) to produce the preliminary answer (e.g.,factual likelihood of a topic), and generating the answer quality levelbased on the preliminary answer and the request (e.g., the IEI request244, the query request 136).

When the answer quality level is unfavorable, the IEI module 122 issuesa collections request 132 to the collections module 120 to gather morecontent to produce knowledge to enable a desired favorable quality levelof the answer. The issuing includes generating the collections request132 based on one or more of the IEI requests 244, the preliminaryanswer, elements of the fact base information 600 (e.g., the presentknowledge base), and the answer quality level.

The collections module 120 interprets one or more collections requests132 to produce content requirements. The interpreting includes one ormore of determining content selection requirements, determining sourceselection requirements, and determining content acquisition timingrequirements. For example, the collections module 120 determines thesource selection requirements to include selecting the content sources16-1 through 16-N of the media content sources 780, determines thecontent selection requirements to include content associated with thefactual likelihood of a topic, and determines the content acquisitiontiming requirements to include a one hour time span.

Having produced the content requirements, the collections module 120issues a plurality of content requests 126 to a plurality of mediasources identified by the content requirements (e.g., to the contentsources 16-1 through 16-N). For example, the collections module 120identifies the plurality of media sources, generates the contentrequests 126 based on the content requirements, and sends the pluralityof content requests 126 to the identified plurality of content sources16-1 through 16-N.

Having issued the plurality of content requests 126, the collectionsmodule 120 interprets a plurality of content responses 128 to determinewhether a response quality level is favorable. The interpreting includesanalyzing the plurality of content responses 128 to produce an estimatedresponse quality level, and indicating a favorable response qualitylevel when the estimated response quality level compares favorably to aminimum response quality threshold level (e.g., greater than). When theresponse quality level is favorable, the collections module 120 issues acollections response 134 to the IEI module 122, where the collectionsresponse 134 includes further content. For example, the collectionsmodule 120 generates the collections response 134 to include the furthercontent and the estimated response quality level, and sends thecollections response 134 to the IEI module 122.

The IEI module 122 analyzes the further content based on one or more ofthe IEI request 244 and the fact base information 600 to produce one ormore of updated fact base information (e.g., new knowledge for storagein the SS memory 96) and a preliminary answer with an associatedpreliminary answer quality level. For example, the IEI module 122reasons the further content with the fact base information 600 toproduce the preliminary answer which identifies the factual likelihoodof a topic. When the answer quality level is favorable, the IEI module122 issues an IEI response 246 to the query module 124 where the IEIresponse 246 includes the preliminary answer associated with a favorableanswer quality level. The query module 124 interprets the receivedanswer to produce a quality level of the received answer. For example,the query module 124 analyzes the preliminary answer in accordance withthe query requirements and the rules to generate the quality level ofthe received answer. When the quality level of the received answer isfavorable, the query module 124 issues a query response 140 to thetransactional server 18-1, where the query response 140 includes theanswer associated with the favorable quality level of the answer.

FIG. 12B is a data flow diagram for providing an answer to a questionwith regards to factual likelihood within a computing system usingcurated knowledge. The data flow diagram includes the IEI module 122 ofFIG. 12A and fact base information 600 in the form of the media sources790. The media sources 790 includes a plurality of source MI-MNgroupings table 792. Each groupings table 792 includes multiple fieldsincluding fields for a group (GRP) identifier (ID) 586, word strings588, identigen (IDN) string 626, and an entigen (ENI) 628. For instance,the groupings tables 792 of the media sources 790 includes word stringsand identifiers associated with factual likelihood of a topic includinginstances of B and C being true about A, and instances when B is nottrue about A and C is not true about A.

As an example of operation of providing an answer to a query, the IEImodule 122 interprets the IEI request 244, facilitates obtaining thefact base information 600, and generates the preliminary answer based onthe rules 316 and associated time frames relevant to the question of theIEI request 244. For example, the IEI module 122 generates thepreliminary answer to indicate that “B is likely true about A”. Forinstance, the IEI module 122 identifies that B is true about A in aninstance and B is likely true about A in another instance associatedwith the timeframe. FIGS. 12C-D are data flow diagrams for curatingknowledge within a computing system.

FIG. 12C illustrates the curating of the knowledge and includes step 793where an initial interpretation is generated for raw content (e.g.,source content 310). For example, a plurality of phrases, including trueand false phrases, of a related topic (e.g., an aspect of currentevents, a historical topic, a topic requiring interpretation, etc.) areingested from a plurality of sources 1-S, including trusted andun-trusted sources. The characters of strings of words of the phrasesare compared to entries of a dictionary to produce valid words (e.g.,known words). The valid words are compared to entries of an identigenlist (e.g., from a knowledge database) to produce, for each word, a setof identigens (e.g., possible meanings of the word). Language specificrules are applied to the identified identigens with regards to orderingof the identigens (e.g., simple pairs of identigens to complex stringsof identigens) to determine the validity of various combinations of theidentigens to produce entigen groups for each phrase, where each entigengroup represents a most likely meaning of the corresponding phrase.

Generating the initial interpretation from the entigen groups includes avariety of approaches. One approach includes identifying a most commonmeaning of the entigen groups. Another approach includes identifying anentigen group that compares favorably to a search phrase (e.g., buzzwordsearch).

The curating of the knowledge continues at step 794 where the initialinterpretation is scored to determine whether the initial interpretationis reliable based on phrases gathered so far. For example, each entigengroup is analyzed to determine whether it supports the initialinterpretation as a confirming entigen group or the opposite, where theentigen group provides negative support for the initial interpretationas he does confirming entigen group. Still other entigen groups may beneutral and not confirm or disconfirm the initial interpretation. Eachentigen group is scored based on its affiliation as a confirming or doesconfirming entigen group, age of the phrase associated with the entigengroup (e.g., fresher data may be more reliable), and a historical recordof reliability of the source associated with the phrase of the entigengroup. For instance, a score associated with a more favorable level ofconfidence is associated with an entigen group that aligns with theconfirming of the initial interpretation and is based on newerinformation from more reliable sources.

The curating of the knowledge continues at step 795 where the scores areinterpreted to determine whether the initial interpretation is reliableand, when reliable, adds the initial interpretation to a knowledgedatabase. For example, a weighting approach is utilized to aggregatescores to produce a confidence level. For instance, weighting factorsare multiplied by each component of the scores (e.g., an alignmentcomponent, the source reliability component, an information agecomponent) to produce intermediate scores for aggregation to produce theconference level. The confidence level indicates that the initialinterpretation is reliable when the confidence level is greater than aconfidence threshold.

When the initial interpretation is reliable, one or more of the initialinterpretation and the entigen groups are added to the knowledge base tocreate the updated fact base information 608. Additional knowledge isadded to previous knowledge to create the curated knowledge.

FIG. 12D illustrates an example of the curated knowledge, where rawcontent (e.g., source content 310) is ingested and analyzed to produceuncurated knowledge 798. The uncurated knowledge 798 is scored forfurther analysis of the scores to produce curated knowledge (fact baseinformation 600) when a confidence level based on the scores indicatesthat the initial interpretation 801 of the uncurated knowledge 798 isreliable.

In an example, phrases 800-1 through 800-3 are received from knownreliable sources S1-S3, phrases 800-11 through 800-13 are received fromunknown reliable sources S11 through S13, and phrases 800-41 through800-43 are received from known unreliable sources S41 through S43, wherethe phrases are associated with a related topic. Each phrase isprocessed to produce a corresponding entigen group, wherein each entigengroup represents a most likely meaning of the phrase. The initialinterpretation 801 is produced based on the entigen groups (e.g., a mostfrequent most likely meaning).

To score the un-curated knowledge 798, each entigen group is affiliatedwith one of confirming entigen groups 802 (e.g., when the entigen groupconfirms the initial interpretation 801), inconclusive entigen groups804 (e.g., when the entigen group neither confirms or disconfirm theinitial interpretation 801), and disconfirming entigen groups 806 (e.g.,when the entigen group does confirms the initial interpretation 801).For instance, the confirming entigen groups 802 includes entigen groups810-001 through 810-200, the inconclusive entigen groups 804 includesentigen groups 810-301 through 810-320, and the disconfirming entigengroups 806 includes entigen groups 810-401 through 810-405. Each entigengroup is associated with a score based on one or more of affiliationwith one of the entigen groups 801, 804, and 806, age of the associatedphrase, and reliability of the source associated with the phrase. Forexample, more favorable (e.g., higher scores) are assigned to entigengroups that confirm the initial interpretation and that are from knownreliable sources.

A weighted scoring approaches applied to the scores for the entigengroups to produce a confidence level of the initial interpretation 801.For example, a sum of the scores of the confirming entigen groups 802 ismultiplied by a confirming weighting factor to produce a confirmingintermediate confidence level, the sum of the scores of the inconclusiveentigen groups 804 is multiplied by an inconclusive weighting factor toproduce an inconclusive intermediate confidence level, and a sum of thescores of the disconfirming entigen groups 806 is multiplied by adisconfirming weighting factor to produce a disconfirming intermediateconference level. The disconfirming intermediate confidence level issubtracted (e.g., to lower an overall conference level) from a sum ofthe confirming intermediate confidence level and the inconclusiveintermediate confidence level to produce the overall confidence level.

When the confidence level is greater than a confidence threshold level,a reliable interpretation 807 is produced based on the initialinterpretation 801 (e.g., the same, modified based on some of theentigen groups) and confirmed entigen groups 808 are produced to includeat least some of the entigen groups associated with the confirmingentigen groups 802.

FIG. 12E is a logic diagram of an embodiment of a method for curatingknowledge within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-8L, and also 12A-12D. The methodincludes step 812 where a processing module of one or more processingmodules of one or more computing devices of the computing systemgenerates generating a plurality of entigen groups from a plurality ofphrases. The plurality of entigen groups represents a plurality of mostlikely meanings for the plurality of phrases. The plurality of phrasesis of a related topic.

The generating the plurality of entigen groups from the plurality ofphrases includes a series of generating steps. As an example ofprocessing a first phrase, a first generating step includes determininga set of identigens for each word of at least some words of a string ofwords of the first phrase of the plurality of phrases to produce aplurality of sets of identigens. Each identigen of the set of identigensis a different meaning of a corresponding word. Each phrase is processedin a similar manner.

A second generating step includes interpreting, based on a knowledgedatabase, the plurality of sets of identigens to produce the firstentigen group. Each entigen of the first entigen group corresponds to aselected identigen of one of the plurality of sets of identigens thatrepresents a most likely meaning of a corresponding word of the at leastsome of the words of the string of words. The first entigen group is amost likely meaning of the string of words. The knowledge databaseincludes a plurality of records that link words having a connectedmeaning. For example, a graphical database is utilized to represententigens and linkages between the entigens.

The method continues at step 813 where the processing module determinesan initial interpretation of the related topic based on the plurality ofmost likely meanings for the plurality of phrases. The determining theinitial interpretation of the related topic based on the plurality ofmost likely meanings for the plurality of phrases includes utilizing oneor more of a variety of interpretation approaches.

A first interpretation approach includes identifying a most frequentmost likely meaning of the plurality of most likely meanings for theplurality of phrases as the initial interpretation. For example, theprocessing module stratified is the entigen groups by their associatedmost likely meaning and identifies the most likely meaning that occursmore often than others.

A second interpretation approach includes identifying an entigen groupassociated with the most frequent most likely meaning of the pluralityof most likely meanings for the plurality of phrases as the initialinterpretation. For example, the processing module identifies an entigengroup that corresponds to the identified most frequent most likelymeaning.

The third interpretation approach includes identifying an entigen groupassociated with a most likely meaning that compares favorably to asearch phrase as the initial interpretation. For example, the processingmodule obtains the search phrase (e.g., a buzzword, a string of words),produces a search phrase entigen group using the search phrase, andcompares the search phrase entigen group to the plurality of entigengroups to identify an entigen group that compares favorably to thesearch phrase entigen group.

The method continues at step 814 where the processing module generates aplurality of scores for the plurality of entigen groups based on theinitial interpretation of the related topic and source information(e.g., source reliability, age of phrase) of the plurality of phrases. Afirst score of the plurality of scores is for a first entigen group ofthe plurality of entigen groups. The generating of the scores includesutilizing one or more of a variety of score generating approaches.

A first score generating approach includes determining a reliabilityscore for the first entigen group based on a reliability level of afirst source associated with a first phrase that is utilized to generatethe first entigen group. For example, the processing module obtains ahistorical record of the reliability level of the first source toproduce the reliability score.

A second score generating approach includes determining an aging scorefor the first entigen group based on an age of the first phrase. Forexample, the processing module obtains a freshness level (e.g., atimestamp of generation of the first phrase, a timestamp of receipt ofthe first phrase, a timeframe between generation of the phrase and acurrent time, a timeframe between receipt of the phrase and a currenttime) and calculates the aging score utilizing the freshness level,where an aging score for an older phrase is less favorable (e.g., lessthan) that an aging score for a newer phrase.

A third score generating approach includes determining an alignmentscore for the first entigen group based on alignment with the initialinterpretation. The alignment score for a confirming alignment isgreater than (e.g., more favorable) an alignment score for adisconfirming alignment. For example, the processing module compares themost likely meaning of the first entigen group to the initialinterpretation and indicates the confirming alignment when thecomparison is favorable (e.g., the entigen group supports the initialinterpretation). As another example, the processing module indicatesdisconfirming alignment when the comparison is unfavorable (e.g., theentigen group opposes the initial interpretation). As yet anotherexample, the processing module indicates neutral alignment when thecomparison is neither favorable or unfavorable (e.g., the entigen groupdoes not support or oppose the initial interpretation).

A fourth score generating approach includes determining the first scorefor the first entigen group based on a weighting approach and thereliability score for the first entigen group, the aging score for thefirst entigen group, and the alignment score for the first entigengroup. The weighting approaches includes establishing higher weightingto the reliability score when the first source has a superior historicalrecord of issuing true phrases and establishes higher weighting to agewhen information freshness matters more.

The method continues at step 815 where the processing module interpretsthe plurality of scores in relation to the initial interpretation todetermine a confidence level of the initial interpretation. Theinterpreting of the plurality of scores includes a series ofinterpreting steps.

A first interpreting step includes identifying confirming entigen groupsof the plurality of entigen groups favorably aligned with the initialinterpretation. For example, the processing module counts the number ofentigen groups that support the initial interpretation.

A second interpreting step includes identifying disconfirming entigengroups of the plurality of entigen groups unfavorably aligned with theinitial interpretation. For example, the processing module counts thenumber of entigen groups that oppose the initial interpretation.

A third interpreting step includes determining the confidence levelbased on a weighting approach and scores for the confirming entigengroups and other scores for the disconfirming entigen groups. Forexample, the processing module multiplies each score by a weightingfactors for confirming and disconfirming to produce an intermediateconfidence level and aggregates the intermediate confidence levels toproduce the confidence level.

When the confidence level of the initial interpretation comparesunfavorably to a confidence threshold, the method branches to step 818(e.g., the processing module indicates the unfavorable comparison whenthe confidence level is less than the confidence threshold). When theconference level of the initial interpretation compares favorably to theconfidence threshold, the method continues at step 816 (e.g., theprocessing module indicates the favorable comparison when the confidencelevel is greater than the confidence threshold).

When the confidence level of the initial interpretation comparesfavorably to a confidence threshold, the method continues at step 816where the processing module indicates that the initial interpretation isreliable (e.g., ready for further processing is curated knowledge). Themethod continues at step 817 where the processing module stores arepresentation of the initial interpretation in a knowledge database ascurated knowledge. For example, the processing module stores and theinitial interpretation entigen group as the curated knowledge in theknowledge database. As another example, the processing module stores atleast some of the plurality of entigen groups in the knowledge databaseas further curated knowledge (e.g., entigen groups that support theinitial interpretation).

When the confidence level of the initial interpretation comparesunfavorably to the confidence threshold, the method continues at step818 where the processing module facilitates one or more further steps togather and process more uncurated knowledge to lead to producing curatedknowledge by looping back to step 812.

A first further step includes generating an updated plurality of entigengroups from an updated plurality of phrases. The updated plurality ofentigen groups represents a plurality of most likely meanings for theupdated plurality of phrases. The updated plurality of phrases is of therelated topic (e.g., the same topic when trying to curate knowledgearound the related topic, alternatively a different related topic whenseveral loops have occurred without generating curated knowledge).

A second further step includes determining an updated initialinterpretation of the related topic based on the plurality of mostlikely meanings for the updated plurality of phrases. For instance, theprocessing module modifies the initial interpretation to produce theupdated initial interpretation based on more insights from the updatedplurality of entigen groups.

A third further step includes generating an updated plurality of scoresfor the updated plurality of entigen groups based on the updated initialinterpretation of the related topic and updated source information(e.g., new sources, new timing) of the updated plurality of phrases. Afirst score of the updated plurality of scores is for a first entigengroup of the updated plurality of entigen groups. For example, theprocessing module re-scores previously scored entigen groups and ascience a score to new entigen groups resulting from the furthergathering of more phrases.

A fourth further step includes interpreting the updated plurality ofscores in relation to the updated initial interpretation to determine anupdated confidence level of the updated initial interpretation. Forexample, the processing module recalculates the confidence level basedon all of the newly collected knowledge (e.g., the updated plurality ofentigen groups etc.)

A fifth further step includes, when the updated confidence level of theupdated initial interpretation compares favorably to the confidencethreshold, the processing module indicates that the updated initialinterpretation is reliable. For example, the processing module utilizesthe same confidence threshold with the new curated knowledge todetermine whether the comparison is favorable. As another example, theprocessing module utilizes an updated confidence threshold (e.g., alower threshold level) to determine whether the updated initialinterpretation is reliable.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 13A is a schematic block diagram of another embodiment of acomputing system that includes language sources 820, the user device12-1 of FIG. 1, and the artificial intelligence (AI) server 20-1 ofFIG. 1. The language sources 820 includes the content sources 16-1through 16-N of FIG. 1. In particular, the content sources 16-1 through16-N provides one or more of dictionaries, language translationreferences, dialect information, cultural information, historicallanguage evolution information, regional language information, spokenversus written language similarities and differences information,entertainment media, social media information, newsfeeds, pressreleases, blog info, periodicals, library info, records, etc.

The AI server 20-1 includes the processing module 50-1 of FIG. 2 and thesolid state (SS) memory 96 of FIG. 2. The processing module 50-1includes the collections module 120 of FIG. 4A, the identigen entigenintelligence (IEI) module 122 of FIG. 4A, and the query module 124 ofFIG. 4A. Generally, an embodiment of this invention presents solutionswhere the computing system functions to produce a response to a querywith regards to translating a phrase from one language into one or moreother target languages.

In an example of operation of the responding to the query, the querymodule 124 interprets a received query request 136 (e.g., from the userdevice 12-1) to produce query requirements. The interpreting includesone or more of determining content requirements, determining sourcerequirements, determining answer timing requirements, and identifying atleast one domain associated with the query request 136. For example, thequery module 124 determines the content requirements to include a querywith regards to translating a phrase from one language into one or moreother target languages, determines the source requirements to includethe language sources 820, determines the answer timing requirements toinclude current times with regards to utilizing languages, andidentifies language translation as the domain when receiving the queryrequest 136 that includes a question “how does this [first languagephrase] translate into [other language(s)] given associated [context]?”

Having produced the query requirements, the query module 124 issues atleast one of an IEI request 244 and a collections request 132 based onthe query request 136. For example, the query module 124 generates theIEI request 244 and sends the IEI request 244 to the IEI module 122 whenthe source requirements suggest that the IEI module 122 is able toprovide an immediate response. As another example, the query module 124generates the collections request 132 and sends the collections request132 to the collections module 120 when the source requirements suggestthat an immediate update of language utilization is associated with thequery request 136 and more content is required. For instance, the querymodule 124 issues the collections request 132 to the collections module120 to facilitate collecting content and subsequently issues the IEIrequest 244 to the IEI module 122 to generate the response to the query.

When receiving the IEI request 244, the IEI module 122 formats the IEIrequest 244 to produce human expressions that include question contentand question information. The formatting includes analyzing the IEIrequest 244 for recognizable human expressions (e.g., strings of words)of question content and question information in accordance with rulesand fact base information 600 (e.g., language translation informationsuch as word meanings, grammar rules, local dialect nuances, contextshifters, etc.) obtained from the SS memory 96.

Having produced the human expressions, the IEI module 122 applies “IEIprocessing” to the human expressions to produce one or more of newknowledge, a preliminary answer, and an answer quality level associatedwith the preliminary answer. The IEI processing includes identifyingpermutations of identigens, reducing the permutations in accordance withthe rules, mapping the reduced permutations of identigens to entigens togenerate knowledge, processing the knowledge in accordance with the factbase (e.g., fact base info 600) to produce the preliminary answer (e.g.,a language translation output), and generating the answer quality levelbased on the preliminary answer and the request (e.g., the IEI request244, the query request 136).

When the answer quality level is unfavorable, the IEI module 122 issuesa collections request 132 to the collections module 120 to gather morecontent to produce knowledge to enable a desired favorable quality levelof the answer. The issuing includes generating the collections request132 based on one or more of the IEI requests 244, the preliminaryanswer, elements of the fact base information 600 (e.g., the presentknowledge base), and the answer quality level.

The collections module 120 interprets one or more collections requests132 to produce content requirements. The interpreting includes one ormore of determining content selection requirements, determining sourceselection requirements, and determining content acquisition timingrequirements. For example, the collections module 120 determines thesource selection requirements to include selecting the content sources16-1 through 16-N of the language sources 820, determines the contentselection requirements to include content associated with languagetranslation and determines the content acquisition timing requirementsto include an immediate analysis.

Having produced the content requirements, the collections module 120issues a plurality of content requests 126 to a plurality of languagesources identified by the content requirements (e.g., to the contentsources 16-1 through 16-N). For example, the collections module 120identifies the plurality of language sources, generates the contentrequests 126 based on the content requirements, and sends the pluralityof content requests 126 to the identified plurality of content sources16-1 through 16-N.

Having issued the plurality of content requests 126, the collectionsmodule 120 interprets a plurality of content responses 128 to determinewhether a response quality level is favorable. The interpreting includesanalyzing the plurality of content responses 128 to produce an estimatedresponse quality level, and indicating a favorable response qualitylevel when the estimated response quality level compares favorably to aminimum response quality threshold level (e.g., greater than). When theresponse quality level is favorable, the collections module 120 issues acollections response 134 to the IEI module 122, where the collectionsresponse 134 includes further content. For example, the collectionsmodule 120 generates the collections response 134 to include the furthercontent and the estimated response quality level, and sends thecollections response 134 to the IEI module 122.

The IEI module 122 analyzes the further content based on one or more ofthe IEI request 244 and the fact base information 600 to produce one ormore of updated fact base information (e.g., new knowledge for storagein the SS memory 96) and a preliminary answer with an associatedpreliminary answer quality level. For example, the IEI module 122reasons the further content with the fact base information 600 toproduce the preliminary answer which produces the language translation.When the answer quality level is favorable, the IEI module 122 issues anIEI response 246 to the query module 124 where the IEI response 246includes the preliminary answer associated with a favorable answerquality level.

The query module 124 interprets the received answer to produce a qualitylevel of the received answer. For example, the query module 124 analyzesthe preliminary answer in accordance with the query requirements and therules to generate the quality level of the received answer. When thequality level of the received answer is favorable, the query module 124issues a query response 140 to the transactional server 18-1, where thequery response 140 includes the answer associated with the favorablequality level of the answer.

FIGS. 13B-13D are process flow diagrams of another embodiment of amethod to translate words of a first language into words of a secondlanguage within a computing system. The method includes interpretingtrue meaning of a sentence for translation and transforming the truemeaning into a sentence of another language.

The interpreting of the true meaning of the sentence for translationincludes a series of interpreting steps. FIG. 13B illustrates a firstinterpreting step includes identifying textual words 528-1 (e.g., in thefirst language) utilizing a dictionary associated with the firstlanguage. For example, the words “the”, “black”, “bat”, “eats”, and“fruit” are identified as valid words when the sentence includes: “Theblack that eats fruit.”

A second interpreting the step includes identifying grammatical use649-1 (e.g., for the first language, English in a specific example),where the ordering of the words establishes grammatical use inaccordance with norms for the first language. A third interpreting stepincludes identifying a word type 542 (e.g., object, characteristic,action, functional) for each word in accordance with the first language.For example, “black” is a color characteristic, “bat” is an object or anaction, “eats” is an action, and “fruit” is an object.

A fourth interpreting step includes, for each word, listing possibleidentigens 718-1 (e.g., with different meanings in the first language).For example, a knowledge database includes a list of all possibleidentigens for known words of the first language.

A fifth interpreting step includes selecting, for each word, acorresponding identigen to produce an entigen resulting in a most likelymeaning entigen group 823-1. The selecting includes utilizing firstlanguage rules (e.g., which pairings, groupings, and ordering of two ormore identigens are allowed in accordance with the first language) topare down the permutations of identigens to select the survivingentigens. For example, entigen e452 corresponding to a flying bat isselected since a baseball bat is eliminated since the first languagerules do not include a baseball bat eating anything and the flying batcan eat fruit. In a similar manner, entigen e3282 is selected for thefirst language word “black”, entigen e7398 is selected for the firstlanguage word “eats”, and entigen e8272 is selected for the firstlanguage word “fruit.”

The most likely meaning entigen group 823-1 is language independent eventhough it was generated from the first language. Each entigen of themost likely meaning entigen group is linked by a connected meaning thatis language independent. For example, the bat “is” of the black color,the bat “does” eat, and the eating “does to” the fruit. The most likelymeaning entigen group and 23-1 may be further integrated with aknowledge database to build the knowledge database and/or verify thatthe most likely meaning entigen group is valid. For example, the blackthat eats fruit is integrated into the knowledge database where the batis connected to a flying mammal that also eats insects. The interpretingsteps will be discussed in greater detail with reference to FIG. 13C.

The transforming of the true meaning into a sentence of the otherlanguage includes a series of transformation steps. For each desiredsecond language, the most likely meaning entigen group 823-1 isprocessed utilizing associative meanings (e.g., identigens 718-2 of thesecond language Spanish) to produce, for each entigen of the most likelymeaning entigen group, a set of identigens of the second language, wherethe meanings of the set of identigens of the second language are similarto the meaning of the entigen.

The resulting groupings of identigens are processed using grammaticaluse 649-2 (e.g., logical associations of words that map to the selectedidentigens) to produce textual words 528-2 in the second language. Forinstance, the Spanish words “el murciélago negro come fruta” areproduced utilizing the Spanish grammar rules when applied to thepermutations of identigens for Spanish.

In a similar manner, the most likely meaning entigen group 823-1 may beutilized to produce textual words 528-3 for a third language (e.g.,German) by utilizing grammatical rules 649-3 for third language Germanand associated meaning identigens 718-3 for their language German. Forinstance, the German words “die schwarze Fledermaus isst obst” areproduced utilizing the German grammar rules when applied to thepermutations of identigens for German. The translation may include anynumber of languages and dialects. Alternatively, or in addition to, theoutput textual words may be applied to the interpreting steps toreproduce the most likely meaning entigen group for verification of thetranslation process. The verification step will be discussed in greaterdetail with reference to FIG. 13D.

FIG. 13C further illustrates the interpreting steps and transformation,where the English input for translation “The black that eats fruit” isprocessed to determine first language identigens 824-1. For example, theidentigen I1-10 is identified for “black”, identigens I1-438 (e.g.,baseball bat), I1-390 (e.g., flying bat), and I1-238 (e.g., action tohit) are identified for “bat”, identigen I1-940 is identified for“eats”, and identigen I1-829 is identified for “fruit” utilizing aknowledge database associated with the English language.

The plurality of sets of first language identigens 824-1 is interpretedto produce an entigen group 823-1 as a representation of the truemeaning of the English input for translation. For example, entigen e3282is selected for the English language word “black”, entigen e7398 isselected for the English language word “eats”, and entigen e8272 isselected for the English language word “fruit” in accordance with thefirst language rules pertaining to valid orderings and pairings of firstlanguage words.

A plurality of second language identigens 825-1 are identified for theentigen group 823-1, where the second language identigens are associatedwith similar or the same meanings as the entigens. Words of the secondlanguage (e.g., Spanish) are produced based mapping the plurality ofsecond language identigens 825-2 to second language words using secondlanguage rules. In a first step, that may produce words in an incorrectorder for the second language. For example, a simple mapping of theidentigens for black that eats fruit to the corresponding Spanish wordswill have the words “el negro murciélago come fruta” which isnoncompliant to a typical ordering of the words when utilizing theSpanish language.

When the incorrect ordering has occurred, the correct ordering isprovided by applying the second language rules once again to theincorrectly ordered words to produce the correctly ordered words. Forexample, “el negro murciélago come fruta” is rearranged to produce “elmurciélago negro come fruta” in accordance with the second languagerules for Spanish.

FIG. 13D illustrates an example of the verification of the translation,that includes the interpretation and translation steps as previouslydiscussed (e.g., producing “el murciélago negro come fruta” inaccordance with the second language rules for Spanish).

The words of the second language are processed to determine, for eachword of the translation, a set of second language identigens of aplurality of sets of second language identigens 824-2. For example,identigens I2-447 (e.g., baseball bat), I2-831 (e.g., flying bat), andI2-647 (e.g., to hit) are identified for “murciélago” since they are allsimilar. Further, identigen I2-10 is identified for “negro”, identigenI2-355 is identified for “come”, and identigen I2-774 is identified for“fruta” in accordance with second language rules.

The plurality of sets of second language identigens 824-2 areinterpreted to produce an entigen group 823-2. For example, theidentigens are interpreted to produce entigens e3282, e452, e7398, ande8272 in accordance with the second language rules. For instance, foreach entigen, an entigen is selected that has a meaning that mostclosely matches the meaning of a selected identigen of a correspondingset of second language identigens.

The entigen group 823-2 is compared to the entigen group 823-1 and whenthe comparison is favorable (e.g., substantially the same) the output ofthe translation is verified. Alternatively, or in addition to, theentigen group 823-2 is utilized to create a string of words in theEnglish language for comparison to the original English input fortranslation as an alternative verification approach. As a still furtheralternative, the entigen group 823-2 is utilized to generate a string ofwords in a fourth language where the string of words of the fourthlanguage is ingested and translated into the words of the secondlanguage for verification of the translation.

FIG. 13E is a logic diagram of an embodiment of a method for translatingwords of a first language into words of a second language within acomputing system. In particular, a method is presented for use inconjunction with one or more functions and features described inconjunction with FIGS. 1-8L, and also FIGS. 13A-D. The method includesstep 830 where a processing module of one or more processing modules ofone or more computing devices of the computing system interprets, basedon first language rules, a plurality of sets of first languageidentigens to produce an entigen group. The entigen group represents amost likely meaning of a string of first language words. A set of theplurality of sets of first language identigens includes one or moredifferent meanings of a word of the string of first language words.

An entigen of the entigen group corresponds to an identigen of the setof first language identigens having a selected meaning of the differentmeanings of the word. For example, the processing module uses adictionary to identify words of the string of first language words,performs a lookup (e.g., in a knowledge database) of the words toidentify each set of first language identigens for each word of thestring of words, and applies the first language rules to excludedisallowed combinations of first language identigens and to include theallowed combinations of first language identigens to produce the entigengroup.

The method continues at step 832 where the processing module identifies,for each entigen of the entigen group, a corresponding set of secondlanguage identigens to identify a plurality of sets of second languageidentigens. For example, the processing module accesses, for eachentigen of the entigen group, the knowledge database to recover thecorresponding set of second language identigens. The knowledge databaseincludes a plurality of records that link words having a connectedmeaning. For example, the processing module finds the identigen(s) witha same meaning from a record of the knowledge for the entigen.

The method continues at step 834 where the processing module selects,for each entigen of the entigen group, a selected second languageidentigen from the corresponding set of second language identigens basedon meaning of the entigen to produce an initial string of secondlanguage words. For example, the processing module identifies, for eachentigen of the entigen group, the selected second language identigenfrom the corresponding set of second language identigens to produce asecond language identigen group when a meaning of the selected secondlanguage identigen compares favorably to the meaning of the entigenbased on second language rules. For instance, the processing modulefinds the identigen(s) with the same meaning from the record of theknowledge database as the entigen.

The producing of the initial string of second language words furtherincludes mapping each selected second language identigen of the secondlanguage identigen group to a word of the initial string of secondlanguage words based on the second language rules. For example, theprocessing module performs perform a lookup of each word for eachidentigen, selects a best word when there are multiple alternativesbased on the second language rules by reversing the process, where, acandidate string of words is mapped to identigens for comparison to thesecond language identigen group.

The method continues at step 836 for the processing module adjusts,based on the second language rules, the initial string of secondlanguage words to produce a translated string of words having asubstantially similar meaning as the string of first language words. Forexample, the processing module utilizes the second language rules todetermine a re-ordering of the words or different forms of the wordsthat comply with the second language.

The method continues at step 838 where the processing module interprets,based on the second language rules, a plurality of sets of verificationsecond language identigens to produce a verification entigen group. Theverification entigen group represents a most likely meaning of theinitial string of second language words. A set of the plurality of setsof verification second language identigens includes one or moredifferent meanings of a word of the initial string of second languagewords. An entigen of the verification entigen group corresponds to anidentigen of the set of verification second language identigens having aselected meaning of the different meanings of the word. For example, theprocessing module interprets the initial string of second language wordsor a variant, i.e., re-ordering of the words, to produce another entigengroup for comparison to the entigen group produced from the firstlanguage.

When the verification entigen group compares favorably to the entigengroup, the method continues at step 840 where the processing moduleindicates that the initial string of second language words is valid.Alternatively, or in addition to, the processing module generates averification string of first language words for comparison to the stringof first language words using the other entigen group. The processingmodule indicates that the translation is valid when the verificationstring of first language words compares favorably (e.g., substantiallythe same) to the string of first language words.

Alternatively, or in addition to, the processing module identifies, foreach entigen of the entigen group, a corresponding set of third languageidentigens to identify a plurality of sets of third language identigens.The processing module selects, for each entigen of the entigen group, aselected third language identigen from the corresponding set of thirdlanguage identigens based on meaning of the entigen to produce aninitial string of third language words.

When producing the initial string of third language words, theprocessing module may verify the initial string of third language wordsby interpreting the initial string of third language words or a variant,i.e., re-ordering of the words, to produce another entigen group forcomparison to the entigen group produced from the first language. Theprocessing module further generates a verification string of thirdlanguage words for comparison to the string of first language words, oranother string of another language words, using the other entigen group.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 14A is a schematic block diagram of another embodiment of acomputing system that includes route content sources 850, the artificialintelligence (AI) server 20-1 of FIG. 1, and the transactional server18-1 of FIG. 1. The route content sources 850 includes the contentsources 16-1 through 16-N of FIG. 1. In particular, content sourcesassociated with route information provided one or more of trafficmonitor information, wrote sensor information, road conditioninformation, construction information, accident information, publicsafety information, traffic camera feeds, digital short-rangecommunication card data, navigation routing data, destinationinformation, current routing information, social media information,newsfeeds, user activity indicators, user location information, userscheduling information, Internet of things card data, detailed weatherinformation, etc. The AI server 20-1 includes the processing module 50-1of FIG. 2 and the solid state (SS) memory 96 of FIG. 2. The processingmodule 50-1 includes the collections module 120 of FIG. 4A, theidentigen entigen intelligence (IEI) module 122 of FIG. 4A, and thequery module 124 of FIG. 4A. Generally, an embodiment of this inventionpresents solutions where the computing system functions to produce aresponse to a query regarding determining improved route guidance.

In an example of operation of the responding to the query, the querymodule 124 interprets a received query request 136 to produce queryrequirements. The interpreting includes one or more of determiningcontent requirements, determining source requirements, determininganswer timing requirements, and identifying at least one domainassociated with the query request 136. For example, the query module 124determines the content requirements to include routing information,determines the source requirements to include the route content sources850, determines the answer timing requirements to include a timeframeassociated with the routing, and obtains as the domain when receivingthe query request 136 that includes a question “is there a better[route] to [destination] from [location]?”

Having produced the query requirements, the query module 124 issues atleast one of an IEI request 244 and a collections request 132 based onthe query request 136. For example, the query module 124 generates theIEI request 244 and sends the IEI request 244 to the IEI module 122 whenthe source requirements suggest that the IEI module 122 is able toprovide an immediate response. As another example, the query module 124generates the collections request 132 and sends the collections request132 to the collections module 120 when the source requirements suggestthat a future time frame is associated with the query request 136 andmore content is required. For instance, the query module 124 issues thecollections request 132 to the collections module 120 to facilitatecollecting content over a timeframe associated with a vehicle travelingto a next interim waypoint of a plurality of waypoints that lead to afinal destination, where the current route may be amended at the nextinterim waypoint of the query request 136 and subsequently issues theIEI request 244 to the IEI module 122 to generate the response to thequery.

When receiving the IEI request 244, the IEI module 122 formats the IEIrequest 244 to produce human expressions that include question contentand question information. The formatting includes analyzing the IEIrequest 244 for recognizable human expressions of question content andquestion information in accordance with rules and fact base information600 (e.g., facts pertaining to alternative routes) obtained from the SSmemory 96.

Having produced the human expressions, the LEI module 122 applies “IEIprocessing” to the human expressions to produce one or more of newknowledge, a preliminary answer, and an answer quality level associatedwith the preliminary answer. The LEI processing includes identifyingpermutations of identigens, reducing the permutations in accordance withthe rules, mapping the reduced permutations of identigens to entigens togenerate knowledge, processing the knowledge in accordance with the factbase (e.g., fact base info 600) to produce the preliminary answer, andgenerating the answer quality level based on the preliminary answer andthe request (e.g., the LEI request 244, the query request 136).

When the answer quality level is unfavorable, the IEI module 122 issuesa collections request 132 to the collections module 120 to gather morecontent to produce knowledge to enable a desired favorable quality levelof the answer. The issuing includes generating the collections request132 based on one or more of the IEI requests 244, the preliminaryanswer, elements of the fact base information 600 (e.g., the presentknowledge base), and the answer quality level.

The collections module 120 interprets one or more collections requests132 to produce content requirements. The interpreting includes one ormore of determining content selection requirements, determining sourceselection requirements, and determining content acquisition timingrequirements. For example, the collections module 120 determines thesource selection requirements to include selecting the content sources16-1 through 16-N of the route content sources 850, determines thecontent selection requirements to include content associated with theroute guidance (e.g., estimated travel times for each alternativeroute), and determines the content acquisition timing requirements toinclude a time span for collection if any (e.g., within a timeframe thatit takes for a vehicle to travel to the next interim waypoint where arerouting can be implemented).

Having produced the content requirements, the collections module 120issues a plurality of content requests 126 to a plurality of contentsources identified by the content requirements (e.g., to the contentsources 16-1 through 16-N). For example, the collections module 120identifies the plurality of content sources, generates the contentrequests based on the content requirements, and sends the plurality ofcontent requests to the identified plurality of content sources.

Having issued the plurality of content requests 126, the collectionsmodule 120 interprets a plurality of content responses 128 to determinewhether a response quality level is favorable. The interpreting includesanalyzing the plurality of content responses 128 to produce an estimatedresponse quality level, and indicating a favorable response qualitylevel when the estimated response quality level compares favorably to aminimum response quality threshold level (e.g., greater than). When theresponse quality level is favorable, the collections module 120 issues acollections response 134 to the IEI module 122, where the collectionsresponse 134 includes further content. For example, the collectionsmodule 120 generates the collections response 134 to include the furthercontent and the estimated response quality level, and sends thecollections response 134 to the IEI module 122.

The IEI module 122 analyzes the further content based on one or more ofthe IEI request 244 and the fact base information 600 to produce one ormore of updated fact base information (e.g., new knowledge for storagein the SS memory 96) and a preliminary answer with an associatedpreliminary answer quality level. For example, the IEI module 122reasons the further content with the fact base information 600 toproduce the preliminary answer which indicates whether the current routeis optimal and another route that is highly optimized for reducing timeto destination. When the answer quality level is favorable, the IEImodule 122 issues an IEI response 246 to the query module 124 where theIEI response 246 includes the preliminary answer associated with afavorable answer quality level. The query module 124 interprets thereceived answer to produce a quality level of the received answer. Forexample, the query module 124 analyzes the preliminary answer inaccordance with the query requirements and the rules to generate thequality level of the received answer. When the quality level of thereceived answer is favorable, the query module 124 issues a queryresponse 140 to the user device 12-1, where the query response 140includes the answer associated with the favorable quality level of theanswer.

FIG. 14B is a logic diagram of an embodiment of a method for providingan answer to a question with regards to improved route guidance within acomputing system. In particular, a method is presented for use inconjunction with one or more functions and features described inconjunction with FIGS. 1-8L, 14A, and also FIG. 14B. The method includesstep 860 where a processing module of one or more processing modules ofone or more computing devices of the computing system interprets areceived query request from a requester to produce query requirementswith regards to improved route guidance. The interpreting includes oneor more of determining content requirements, (e.g., to provide animproved route), determining source requirements, determining answertiming requirements, and identifying a domain associated with the queryrequest (e.g., real-time route guidance, estimated future-time routeguidance).

The method continues at step 862 where the processing module IEIprocesses human expressions of the received query request based on afact base generated from previous content to produce a preliminaryanswer with regards to the improved route guidance. The processing mayinclude formatting portions of the query request in accordance withformatting rules to produce recognizable human expressions of contentand question information. For example, the processing module producesthe question information to include a request to determine the improvedroute guidance (e.g., provide an alternative route with improved time todestination over a current route).

The processing further includes identifying permutations of identigenswithin the human expressions, reducing the permutations, mapping thereduced permutations to entigens to produce knowledge, processing theknowledge in accordance with a fact base to produce the preliminaryanswer, and generating an answer quality level associated with thepreliminary answer. For instance, the processing module generates arelatively low answer quality level when the question relates togathering information over a subsequent time frame such that morecontent must be gathered (e.g., with regards to near-term actual trafficconditions) to produce an answer associated with a higher and morefavorable answer quality level (e.g., to estimate future trafficconditions that matter to a vehicle utilizing the route guidance).

When the answer quality level is unfavorable, the method continues atstep 864 where the processing module generates content requirements. Thegenerating of the content requirements includes determining, based onone or more of the query requirements, preliminary answer, and theanswer quality level, one or more of content selection requirements,source selection requirements, and acquisition timing requirements.

The method continues at step 866 where the processing module obtainsfurther content from a plurality of route guidance content sources basedon the content requirements. For example, the processing moduleidentifies the plurality of route guidance content sources, generatescontent requests based on the content requirements, and sends theplurality of content requests to the plurality of identified routeguidance content sources, analyzes a plurality of content responses toproduce an estimated quality level, indicates favorable quality levelwhen the estimated quality level compares favorably to a minimum qualitythreshold level, and indicates unfavorable quality level to facilitatecollecting more content when the estimated quality level comparesunfavorably to the minimum quality threshold level.

The method continues at step 868 where the processing module IEIprocesses human expressions of the further content based on the factbase to produce an updated preliminary answer that includes the improvedroute guidance answer that identifies an improved route considering thecurrent route and alternative routes based on estimations of factorsthat influence travel times. For example, the processing moduleanalyzes, based on one or more of the query request, the fact base infoassociated with the identified domain, and the further content toproduce one or more of updated fact base info (e.g., new knowledge), theupdated preliminary illness diagnosis answer (e.g., likelihood of anillness), and an associated answer quality level. The analyzing mayinclude reasoning the further content with the fact base to produce theupdated fact base info and the preliminary improved route guidanceanswer to include the updated route.

When the updated answer quality level is favorable, the method continuesat step 870 where the processing module issues a query response to therequest are that predicts the likelihood of the illness. The issuingincludes one or more of analyzing the preliminary illness diagnosisanswers in accordance with the query requirements and the rules togenerate the updated quality level, generating the query response toinclude the illness diagnosis answer associated with favorable qualitylevel, and sending the query response to the requester

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 15A is a schematic block diagram of another embodiment of acomputing system that includes action content sources 880, theartificial intelligence (AI) server 20-1 of FIG. 1, and the user device12-1 of FIG. 1. The action content sources 880 includes the contentsources 16-1 through 16-N of FIG. 1. In particular, the action contentsources 880 provides one or more of sales records, transportationrecords, location information, Internet traffic, Internet trafficsummaries, social media information, news outlet sources (e.g., pressreleases, periodicals, radial information, TV news, financial markets,etc.), etc. The AI server 20-1 includes the processing module 50-1 ofFIG. 2 and the solid state (SS) memory 96 of FIG. 2. The processingmodule 50-1 includes the collections module 120 of FIG. 4A, theidentigen entigen intelligence (IEI) module 122 of FIG. 4A, and thequery module 124 of FIG. 4A. Generally, an embodiment of this inventionpresents solutions where the computing system functions to produce aresponse to a query regarding detecting that an action has been invokedto produce a particular outcome.

In an example of operation of the responding to the query, the querymodule 124 interprets a received query request 136 to produce queryrequirements. The interpreting includes one or more of determiningcontent requirements, determining source requirements, determininganswer timing requirements, and identifying at least one domainassociated with the query request 136. For example, the query module 124determines the content requirements to include facts that can lead toprediction of the action, determines the source requirements to includethe action content sources 880, determines the answer timingrequirements to include a timeframe associated with the predictedaction, and identifies a particular type of action as the domain whenreceiving the query request 136 that includes a question “is a predictedaction alert threshold reached for [entity] causing [action, outcome]?”

Having produced the query requirements, the query module 124 issues atleast one of an IEI request 244 and a collections request 132 based onthe query request 136. For example, the query module 124 generates theIEI request 244 and sends the IEI request 244 to the IEI module 122 whenthe source requirements suggest that the IEI module 122 is able toprovide an immediate response. As another example, the query module 124generates the collections request 132 and sends the collections request132 to the collections module 120 when the source requirements suggestthat a future time frame is associated with the query request 136 andmore content is required. For instance, the query module 124 issues thecollections request 132 to the collections module 120 to facilitatecollecting content over the 24 hours associated with a typical action ofthe query request 136 and subsequently issues the IEI request 244 to theIEI module 122 to generate the response to the query.

When receiving the IEI request 244, the IEI module 122 formats the IEIrequest 244 to produce human expressions that include question contentand question information. The formatting includes analyzing the IEIrequest 244 for recognizable human expressions of question content andquestion information in accordance with rules and fact base information600 (e.g., facts pertaining to the action) obtained from the SS memory96.

Having produced the human expressions, the IEI module 122 applies “IEIprocessing” to the human expressions to produce one or more of newknowledge, a preliminary answer, and an answer quality level associatedwith the preliminary answer. The IEI processing includes identifyingpermutations of identigens, reducing the permutations in accordance withthe rules, mapping the reduced permutations of identigens to entigens togenerate knowledge, processing the knowledge in accordance with the factbase (e.g., fact base info 600) to produce the preliminary answer, andgenerating the answer quality level based on the preliminary answer andthe request (e.g., the IEI request 244, the query request 136).

When the answer quality level is unfavorable, the IEI module 122 issuesa collections request 132 to the collections module 120 to gather morecontent to produce knowledge to enable a desired favorable quality levelof the answer. The issuing includes generating the collections request132 based on one or more of the IEI requests 244, the preliminaryanswer, elements of the fact base information 600 (e.g., the presentknowledge base), and the answer quality level.

The collections module 120 interprets one or more collections requests132 to produce content requirements. The interpreting includes one ormore of determining content selection requirements, determining sourceselection requirements, and determining content acquisition timingrequirements. For example, the collections module 120 determines thesource selection requirements to include selecting the content sources16-1 through 16-N of the action content sources 880, determines thecontent selection requirements to include content associated with theaction (e.g., sequences and/or chained indicators that are affiliatedwith the action), and determines the content acquisition timingrequirements to include a time span for collection if any.

Having produced the content requirements, the collections module 120issues a plurality of content requests 126 to a plurality of contentsources identified by the content requirements (e.g., to the contentsources 16-1 through 16-N). For example, the collections module 120identifies the plurality of content sources, generates the contentrequests based on the content requirements, and sends the plurality ofcontent requests to the identified plurality of content sources.

Having issued the plurality of content requests 126, the collectionsmodule 120 interprets a plurality of content responses 128 to determinewhether a response quality level is favorable. The interpreting includesanalyzing the plurality of content responses 128 to produce an estimatedresponse quality level, and indicating a favorable response qualitylevel when the estimated response quality level compares favorably to aminimum response quality threshold level (e.g., greater than). When theresponse quality level is favorable, the collections module 120 issues acollections response 134 to the IEI module 122, where the collectionsresponse 134 includes further content. For example, the collectionsmodule 120 generates the collections response 134 to include the furthercontent and the estimated response quality level, and sends thecollections response 134 to the IEI module 122.

The IEI module 122 analyzes the further content based on one or more ofthe IEI request 244 and the fact base information 600 to produce one ormore of updated fact base information (e.g., new knowledge for storagein the SS memory 96) and a preliminary answer with an associatedpreliminary answer quality level. For example, the IEI module 122reasons the further content with the fact base information 600 toproduce the preliminary answer which predicts the likelihood of theaction being triggered. When the answer quality level is favorable, theIEI module 122 issues an IEI response 246 to the query module 124 wherethe IEI response 246 includes the preliminary answer associated with afavorable answer quality level. The query module 124 interprets thereceived answer to produce a quality level of the received answer. Forexample, the query module 124 analyzes the preliminary answer inaccordance with the query requirements and the rules to generate thequality level of the received answer. When the quality level of thereceived answer is favorable, the query module 124 issues a queryresponse 140 to the user device 12-1, where the query response 140includes the answer associated with the favorable quality level of theanswer.

FIG. 15B is a data flow diagram for generating a predicted action alertwithin a computing system, where a computing device of the computingsystem performs the resolve answer step 644, based on rules 316, time702, and fact base info 600, on content that includes an estimated valueand desired range for each of n conditions for each N sequences toproduce preliminary answers 354. Each condition of the content describesstatus of an outside force that can be determined based on fact baseinfo 600 (e.g., location, statements, detected scenarios, etc.). Thecomputing device compares the estimated value of the condition to adesired range (e.g., minimum/maximum of a metric) associated with thecondition to produce the status (e.g., probability of a factual elementbased on the comparison). Each sequence includes an ordered series ofconditions that are estimated to have values that compare favorably toan associated desired value range to complete the sequence (e.g.,ordering may be strict or flexible). The plurality of sequences mayinclude any number of sequences to link to the occurrence.

In an example of operation, one sequence is utilized with two conditionsto provide an estimated invoking of an action, where the first conditionis a text message from the entity of the request, and the secondcondition is a detected location of the entity within a proximallocation of the location of the query. The computing device obtains thecontent for the first and second conditions, and generates a preliminaryanswer 354 that indicates that the invoking of the action is detected.

FIG. 15C is a logic diagram of an embodiment of a method for generatinga predicted action alert within a computing system. In particular, amethod is presented for use in conjunction with one or more functionsand features described in conjunction with FIGS. 1-8L, 15A-15B, and alsoFIG. 15C. The method includes step 890 where a processing module of oneor more processing modules of one or more computing devices of thecomputing system interprets a received query request from a requester toproduce query requirements with regards to an action and/or outcome(e.g., as a result of the action). The interpreting includes one or moreof determining content requirements, (e.g., to gather conditions ofsequences), determining source requirements, determining answer timingrequirements, and identifying a domain associated with the queryrequest.

The method continues at step 892 where the processing module IEIprocesses human expressions of the received query request based on afact base generated from previous content to produce a preliminaryaction alert answer. The processing may include formatting portions ofthe query request in accordance with formatting rules to producerecognizable human expressions of content and question information. Forexample, the processing module produces the question information toinclude a request to determine likelihood of occurrence of an action(e.g., identifying conditions and scenarios that lead to the action orat least detection of early signs of invoking of the action). Theprocessing may further include identifying permutations of identigenswithin the human expressions, reducing the permutations, mapping thereduce permutations to entigens to produce knowledge, processing theknowledge in accordance with a fact base to produce the preliminaryanswer, and generating an answer quality level associated with thepreliminary answer. For instance, the processing module generates arelatively low answer quality level when the question relates togathering information over a subsequent time frame such that morecontent must be gathered to produce an answer associated with a higherand more favorable answer quality level (e.g., start looking for valuesof conditions associated with scenarios to support answering the actionalert question).

When the answer quality level is unfavorable, the method continues atstep 894 where the processing module generates content requirements. Thegenerating of the content requirements includes determining, based onone or more of the query requirements, preliminary answer, and theanswer quality level, one or more of content selection requirements,source selection requirements, and acquisition timing requirements.

The method continues at step 896 where the processing module obtainsfurther content from a plurality of action content sources based on thecontent requirements. For example, the processing module identifies theplurality of content sources, generates content requests based on thecontent requirements, and sends the plurality of content requests to theplurality of identified content sources, analyzes a plurality of contentresponses to produce an estimated quality level, indicates favorablequality level when the estimated quality level compares favorably to aminimum quality threshold level, and indicates unfavorable quality levelto facilitate collective more content when the estimated quality levelcompares unfavorably to the minimum quality threshold level.

The method continues at step 898 where the processing module IEIprocesses human expressions of the further content based on the factbase to produce an updated preliminary action alert answer indicatingdetection of early signs of the action or clear signs of invoking of theaction. For example, the processing module analyzes, based on one ormore of the query request, the fact base info associated with theidentified domain, and the further content to produce one or more ofupdated fact base info (e.g., new knowledge), the updated preliminaryaction alert answer (e.g., detection of action), and an associatedanswer quality level. The analyzing may include reasoning the furthercontent with the fact base to produce the updated fact base info and thepreliminary answer to include the action alert.

When the updated answer quality level is favorable, the method continuesat step 900 where the processing module issues a query response to therequest ae that includes a likelihood of the action and/or outcome. Theissuing includes one or more of analyzing the preliminary answers inaccordance with the query requirements and the rules to generate theupdated quality level, generating the query response to include theanswer associated with favorable quality level, and sending the queryresponse to the requester

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 16A is a schematic block diagram of another embodiment of acomputing system that includes author sources 910, the artificialintelligence (AI) server 20-1 of FIG. 1, and the user device 12-1 ofFIG. 1. The author sources 910 includes the content sources 16-1 through16-N of FIG. 1. In particular, the author sources 910 provides one ormore of newsfeeds, social media information, press releases, informationfrom blogs, periodical information, library information, generalrecords, video clips, speeches, anything authored by an author, etc. TheAI server 20-1 includes the processing module 50-1 of FIG. 2 and thesolid state (SS) memory 96 of FIG. 2. The processing module 50-1includes the collections module 120 of FIG. 4A, the identigen entigenintelligence (IEI) module 122 of FIG. 4A, and the query module 124 ofFIG. 4A. Generally, an embodiment of this invention presents solutionswhere the computing system functions to produce a response to a queryregarding identifying authorship of a composition.

In an example of operation of the responding to the query, the querymodule 124 interprets a received query request 136 to produce queryrequirements. The interpreting includes one or more of determiningcontent requirements, determining source requirements, determininganswer timing requirements, and identifying at least one domainassociated with the query request 136. For example, the query module 124determines the content requirements to include facts that can identifyauthorship of a composition, determines the source requirements toinclude the author sources 910, determines the answer timingrequirements to include a timeframe associated with the authoring, andidentifies authoring as the domain when receiving the query request 136that includes a question “who authored this [composition], or what isthe likelihood that [author] created this [composition]?”

Having produced the query requirements, the query module 124 issues atleast one of an IEI request 244 and a collections request 132 based onthe query request 136. For example, the query module 124 generates theLEI request 244 and sends the LEI request 244 to the IEI module 122 whenthe source requirements suggest that the IEI module 122 is able toprovide an immediate response. As another example, the query module 124generates the collections request 132 and sends the collections request132 to the collections module 120 when the source requirements suggestthat a future time frame is associated with the query request 136 andmore content is required. For instance, the query module 124 issues thecollections request 132 to the collections module 120 to facilitatecollecting content over the next day associated with generation offurther compositions anticipated by the query request 136 andsubsequently issues the IEI request 244 to the IEI module 122 togenerate the response to the query.

When receiving the IEI request 244, the IEI module 122 formats the IEIrequest 244 to produce human expressions that include question contentand question information. The formatting includes analyzing the IEIrequest 244 for recognizable human expressions of question content andquestion information in accordance with rules and fact base information600 (e.g., facts pertaining to compositions by authors includinglanguage, typical utilization of key words, style characteristics, etc.)obtained from the SS memory 96.

Having produced the human expressions, the IEI module 122 applies “IEIprocessing” to the human expressions to produce one or more of newknowledge, a preliminary answer, and an answer quality level associatedwith the preliminary answer. The IEI processing includes identifyingpermutations of identigens, reducing the permutations in accordance withthe rules, mapping the reduced permutations of identigens to entigens togenerate knowledge, processing the knowledge in accordance with the factbase (e.g., fact base info 600) to produce the preliminary answer, andgenerating the answer quality level based on the preliminary answer andthe request (e.g., the IEI request 244, the query request 136).

When the answer quality level is unfavorable, the IEI module 122 issuesa collections request 132 to the collections module 120 to gather morecontent to produce knowledge to enable a desired favorable quality levelof the answer. The issuing includes generating the collections request132 based on one or more of the IEI requests 244, the preliminaryanswer, elements of the fact base information 600 (e.g., the presentknowledge base), and the answer quality level.

The collections module 120 interprets one or more collections requests132 to produce content requirements. The interpreting includes one ormore of determining content selection requirements, determining sourceselection requirements, and determining content acquisition timingrequirements. For example, the collections module 120 determines thesource selection requirements to include selecting the content sources16-1 through 16-N of the author sources 910, determines the contentselection requirements to include content associated with the authorsand compositions (e.g., compositions known to be composed by particularauthors), and determines the content acquisition timing requirements toinclude a time span for collection if any (e.g., over the next day tocapture further compositions associated with a handful of authors of therequest).

Having produced the content requirements, the collections module 120issues a plurality of content requests 126 to a plurality of contentsources identified by the content requirements (e.g., to the contentsources 16-1 through 16-N). For example, the collections module 120identifies the plurality of content sources, generates the contentrequests based on the content requirements, and sends the plurality ofcontent requests to the identified plurality of content sources.

Having issued the plurality of content requests 126, the collectionsmodule 120 interprets a plurality of content responses 128 to determinewhether a response quality level is favorable. The interpreting includesanalyzing the plurality of content responses 128 to produce an estimatedresponse quality level, and indicating a favorable response qualitylevel when the estimated response quality level compares favorably to aminimum response quality threshold level (e.g., greater than). When theresponse quality level is favorable, the collections module 120 issues acollections response 134 to the IEI module 122, where the collectionsresponse 134 includes further content. For example, the collectionsmodule 120 generates the collections response 134 to include the furthercontent and the estimated response quality level, and sends thecollections response 134 to the IEI module 122.

The IEI module 122 analyzes the further content based on one or more ofthe IEI request 244 and the fact base information 600 to produce one ormore of updated fact base information (e.g., new knowledge for storagein the SS memory 96) and a preliminary answer with an associatedpreliminary answer quality level. For example, the IEI module 122reasons the further content with the fact base information 600 toproduce the preliminary answer which predicts the likelihood that aparticular author composed a particular composition or which identifiesa likely candidate author that authored a particular composition of thequery. When the answer quality level is favorable, the IEI module 122issues an IEI response 246 to the query module 124 where the IEIresponse 246 includes the preliminary answer associated with a favorableanswer quality level. The query module 124 interprets the receivedanswer to produce a quality level of the received answer. For example,the query module 124 analyzes the preliminary answer in accordance withthe query requirements and the rules to generate the quality level ofthe received answer. When the quality level of the received answer isfavorable, the query module 124 issues a query response 140 to the userdevice 12-1, where the query response 140 includes the answer associatedwith the favorable quality level of the answer.

FIG. 16B is a data flow diagram for identifying an author within acomputing system. The data flow diagram includes the IEI module 122 ofFIG. 16A and fact base information 600. The fact base info 600 includesauthor sources 920 organized as a plurality of source A1-AN groupingstables 922. Each groupings table 922 includes multiple fields includingfields for a group (GRP) identifier (ID) 586, word strings 588,identigen (IDN) string 626, and an entigen (ENI) 628. For instance, thegroupings tables 922 includes word strings and identifiers associatedwith authorship.

As an example of operation of providing an answer to a query, the IEImodule 122 interprets the IEI request 244, facilitates obtaining thefact base information 600, and generates the preliminary answer of theIEI response 246 based on the rules 316. For example, the IEI module 122generates the preliminary answer to indicate that “[composition] likelyauthored by author A, when the groupings tables 922 are affiliated withrelevant authorship information. For instance, the IEI module 122indicates that the composition is likely authored by author A when thegroupings tables 922 indicates that author A uses the unique word X33,the author A uses the word pattern Z1, and the author A uses languageL210, when the composition includes the unique word X33, the wordpattern Z1, and is written utilizing the language L210.

FIG. 16C is a logic diagram of an embodiment of a method for identifyingan author within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-8L, 16A-16B, and also FIG. 16C.The method includes step 930 where a processing module of one or moreprocessing modules of one or more computing devices of the computingsystem interprets a received query request from a requester to producequery requirements with regards to identity of an author of acomposition. The interpreting includes one or more of determiningcontent requirements, (e.g., to gather compositions and authorshipinformation), determining source requirements, determining answer timingrequirements, and identifying a domain associated with the queryrequest.

The method continues at step 932 where the processing module IEIprocesses human expressions of the received query request based on afact base generated from previous content to produce a preliminaryanswer with regards to the identity of an author of a composition. Theprocessing may include formatting portions of the query request inaccordance with formatting rules to produce recognizable humanexpressions of content and question information. For example, theprocessing module produces the question information to include a requestto identify the author of the composition. The processing may furtherinclude identifying permutations of identigens within the humanexpressions, reducing the permutations, mapping the reduced permutationsto entigens to produce knowledge, processing the knowledge in accordancewith a fact base to produce the preliminary answer, and generating ananswer quality level associated with the preliminary answer. Forinstance, the processing module generates a relatively low answerquality level when the question relates to gathering information over asubsequent time frame such that more content must be gathered to producean answer associated with a higher and more favorable answer qualitylevel (e.g., look for more compositions that fit a detected pattern ofthe composition associated with the query).

When the answer quality level is unfavorable, the method continues atstep 934 where the processing module generates content requirements. Thegenerating of the content requirements includes determining, based onone or more of the query requirements, preliminary answer, and theanswer quality level, one or more of content selection requirements,source selection requirements, and acquisition timing requirements.

The method continues at step 936 where the processing module obtainsfurther content from a plurality of author content sources based on thecontent requirements. For example, the processing module identifies theplurality of author content sources, generates content requests based onthe content requirements, and sends the plurality of content requests tothe plurality of identified author content sources, analyzes a pluralityof content responses to produce an estimated quality level, indicatesfavorable quality level when the estimated quality level comparesfavorably to a minimum quality threshold level, and indicatesunfavorable quality level to facilitate collecting more content when theestimated quality level compares unfavorably to the minimum qualitythreshold level.

The method continues at step 938 where the processing module IEIprocesses human expressions of the further content based on the factbase to produce an updated preliminary answer that indicates theidentity of an author of a composition. For example, the processingmodule analyzes, based on one or more of the query request, the factbase info associated with the identified domain, and the further contentto produce one or more of updated fact base info (e.g., new knowledge),the updated preliminary answer of authorship (e.g., identity of anauthor), and an associated answer quality level. The analyzing mayinclude reasoning the further content with the fact base to produce theupdated fact base info and the preliminary authorship answer to includethe likelihood of the composition being authored by the identifiedauthor.

When the updated answer quality level is favorable, the method continuesat step 940 where the processing module issues a query response to therequest are that predicts the likelihood of the illness. The issuingincludes one or more of analyzing the preliminary illness diagnosisanswers in accordance with the query requirements and the rules togenerate the updated quality level, generating the query response toinclude the illness diagnosis answer associated with favorable qualitylevel, and sending the query response to the requester

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a computing device, themethod comprises: generating a plurality of entigen groups from aplurality of phrases, wherein the plurality of entigen groups representsa plurality of most likely meanings for the plurality of phrases,wherein the plurality of phrases is of a related topic; determining aninitial interpretation of the related topic based on the plurality ofmost likely meanings for the plurality of phrases; generating aplurality of scores for the plurality of entigen groups based on theinitial interpretation of the related topic and source information ofthe plurality of phrases, wherein a first score of the plurality ofscores is for a first entigen group of the plurality of entigen groups;interpreting the plurality of scores in relation to the initialinterpretation to determine a confidence level of the initialinterpretation; and when the confidence level of the initialinterpretation compares favorably to a confidence threshold, indicatingthat the initial interpretation is reliable.
 2. The method of claim 1further comprises: when the confidence level of the initialinterpretation compares favorably to the confidence threshold,facilitating one or more of: storing a representation of the initialinterpretation in a knowledge database as curated knowledge; and storingat least some of the plurality of entigen groups in the knowledgedatabase as further curated knowledge; and when the confidence level ofthe initial interpretation compares unfavorably to the confidencethreshold, facilitating one or more of: generating an updated pluralityof entigen groups from an updated plurality of phrases, wherein theupdated plurality of entigen groups represents a plurality of mostlikely meanings for the updated plurality of phrases, wherein theupdated plurality of phrases is of the related topic; determining anupdated initial interpretation of the related topic based on theplurality of most likely meanings for the updated plurality of phrases;generating an updated plurality of scores for the updated plurality ofentigen groups based on the updated initial interpretation of therelated topic and updated source information of the updated plurality ofphrases, wherein a first score of the updated plurality of scores is fora first entigen group of the updated plurality of entigen groups;interpreting the updated plurality of scores in relation to the updatedinitial interpretation to determine an updated confidence level of theupdated initial interpretation; and when the updated confidence level ofthe updated initial interpretation compares favorably to the confidencethreshold, indicating that the updated initial interpretation isreliable.
 3. The method of claim 1, wherein the generating the pluralityof entigen groups from the plurality of phrases comprises: determining aset of identigens for each word of at least some words of a string ofwords of a first phrase of the plurality of phrases to produce aplurality of sets of identigens, wherein each identigen of the set ofidentigens is a different meaning of a corresponding word; andinterpreting, based on a knowledge database, the plurality of sets ofidentigens to produce the first entigen group, wherein each entigen ofthe first entigen group corresponds to a selected identigen of one ofthe plurality of sets of identigens that represents a most likelymeaning of a corresponding word of the at least some of the words of thestring of words, wherein the first entigen group is a most likelymeaning of the string of words, wherein the knowledge database includesa plurality of records that link words having a connected meaning. 4.The method of claim 1, wherein the determining the initialinterpretation of the related topic based on the plurality of mostlikely meanings for the plurality of phrases comprises at least one of:identifying a most frequent most likely meaning of the plurality of mostlikely meanings for the plurality of phrases as the initialinterpretation; identifying an entigen group associated with the mostfrequent most likely meaning of the plurality of most likely meaningsfor the plurality of phrases as the initial interpretation; andidentifying an entigen group associated with a most likely meaning thatcompares favorably to a search phrase as the initial interpretation. 5.The method of claim 1, wherein the generating the plurality of scoresfor the plurality of entigen groups based on the initial interpretationof the related topic and source information of the plurality of phrasescomprises one or more of: determining a reliability score for the firstentigen group based on a reliability level of a first source associatedwith a first phrase that is utilized to generate the first entigengroup; determining an aging score for the first entigen group based onan age of the first phrase; determining an alignment score for the firstentigen group based on alignment with the initial interpretation,wherein an alignment score for a confirming alignment is greater than analignment score for a disconfirming alignment; and determining the firstscore for the first entigen group based on a weighting approach and thereliability score for the first entigen group, the aging score for thefirst entigen group, and the alignment score for the first entigengroup.
 6. The method of claim 1, wherein the interpreting the pluralityof scores in relation to the initial interpretation to determine theconfidence level of the initial interpretation comprises: identifyingconfirming entigen groups of the plurality of entigen groups favorablyaligned with the initial interpretation; identifying disconfirmingentigen groups of the plurality of entigen groups unfavorably alignedwith the initial interpretation; and determining the confidence levelbased on a weighting approach and scores for the confirming entigengroups and other scores for the disconfirming entigen groups.
 7. Acomputing device of a computing system, the computing device comprises:an interface; a local memory; and a processing module operably coupledto the interface and the local memory, wherein the processing modulefunctions to: generate a plurality of entigen groups from a plurality ofphrases, wherein the plurality of entigen groups represents a pluralityof most likely meanings for the plurality of phrases, wherein theplurality of phrases is of a related topic; determine an initialinterpretation of the related topic based on the plurality of mostlikely meanings for the plurality of phrases; generate a plurality ofscores for the plurality of entigen groups based on the initialinterpretation of the related topic and source information of theplurality of phrases, wherein a first score of the plurality of scoresis for a first entigen group of the plurality of entigen groups;interpret the plurality of scores in relation to the initialinterpretation to determine a confidence level of the initialinterpretation; and when the confidence level of the initialinterpretation compares favorably to a confidence threshold, indicatethat the initial interpretation is reliable.
 8. The computing device ofclaim 7, wherein the processing module further functions to: when theconfidence level of the initial interpretation compares favorably to theconfidence threshold, facilitate one or more of: storing, via theinterface, a representation of the initial interpretation in a knowledgedatabase as curated knowledge; and storing, via the interface, at leastsome of the plurality of entigen groups in the knowledge database asfurther curated knowledge; and when the confidence level of the initialinterpretation compares unfavorably to the confidence threshold,facilitate one or more of: generating an updated plurality of entigengroups from an updated plurality of phrases, wherein the updatedplurality of entigen groups represents a plurality of most likelymeanings for the updated plurality of phrases, wherein the updatedplurality of phrases is of the related topic; determining an updatedinitial interpretation of the related topic based on the plurality ofmost likely meanings for the updated plurality of phrases; generating anupdated plurality of scores for the updated plurality of entigen groupsbased on the updated initial interpretation of the related topic andupdated source information of the updated plurality of phrases, whereina first score of the updated plurality of scores is for a first entigengroup of the updated plurality of entigen groups; interpreting theupdated plurality of scores in relation to the updated initialinterpretation to determine an updated confidence level of the updatedinitial interpretation; and when the updated confidence level of theupdated initial interpretation compares favorably to the confidencethreshold, indicating that the updated initial interpretation isreliable.
 9. The computing device of claim 7, wherein the processingmodule functions to generate the plurality of entigen groups from theplurality of phrases by: determining a set of identigens for each wordof at least some words of a string of words of a first phrase of theplurality of phrases to produce a plurality of sets of identigens,wherein each identigen of the set of identigens is a different meaningof a corresponding word; and interpreting, based on a knowledgedatabase, the plurality of sets of identigens to produce the firstentigen group, wherein each entigen of the first entigen groupcorresponds to a selected identigen of one of the plurality of sets ofidentigens that represents a most likely meaning of a corresponding wordof the at least some of the words of the string of words, wherein thefirst entigen group is a most likely meaning of the string of words,wherein the knowledge database includes a plurality of records that linkwords having a connected meaning.
 10. The computing device of claim 7,wherein the processing module functions to determine the initialinterpretation of the related topic based on the plurality of mostlikely meanings for the plurality of phrases by at least one of:identifying a most frequent most likely meaning of the plurality of mostlikely meanings for the plurality of phrases as the initialinterpretation; identifying an entigen group associated with the mostfrequent most likely meaning of the plurality of most likely meaningsfor the plurality of phrases as the initial interpretation; andidentifying an entigen group associated with a most likely meaning thatcompares favorably to a search phrase as the initial interpretation. 11.The computing device of claim 7, wherein the processing module functionsto generate the plurality of scores for the plurality of entigen groupsbased on the initial interpretation of the related topic and sourceinformation of the plurality of phrases by one or more of: determining areliability score for the first entigen group based on a reliabilitylevel of a first source associated with a first phrase that is utilizedto generate the first entigen group; determining an aging score for thefirst entigen group based on an age of the first phrase; determining analignment score for the first entigen group based on alignment with theinitial interpretation, wherein an alignment score for a confirmingalignment is greater than an alignment score for a disconfirmingalignment; and determining the first score for the first entigen groupbased on a weighting approach and the reliability score for the firstentigen group, the aging score for the first entigen group, and thealignment score for the first entigen group.
 12. The computing device ofclaim 7, wherein the processing module functions to interpret theplurality of scores in relation to the initial interpretation todetermine the confidence level of the initial interpretation by:identifying confirming entigen groups of the plurality of entigen groupsfavorably aligned with the initial interpretation; identifyingdisconfirming entigen groups of the plurality of entigen groupsunfavorably aligned with the initial interpretation; and determining theconfidence level based on a weighting approach and scores for theconfirming entigen groups and other scores for the disconfirming entigengroups.
 13. A computer readable memory comprises: a first memory elementthat stores operational instructions that, when executed by a processingmodule, causes the processing module to: generate a plurality of entigengroups from a plurality of phrases, wherein the plurality of entigengroups represents a plurality of most likely meanings for the pluralityof phrases, wherein the plurality of phrases is of a related topic; asecond memory element that stores operational instructions that, whenexecuted by the processing module, causes the processing module to:determine an initial interpretation of the related topic based on theplurality of most likely meanings for the plurality of phrases; a thirdmemory element that stores operational instructions that, when executedby the processing module, causes the processing module to: generate aplurality of scores for the plurality of entigen groups based on theinitial interpretation of the related topic and source information ofthe plurality of phrases, wherein a first score of the plurality ofscores is for a first entigen group of the plurality of entigen groups;and a fourth memory element that stores operational instructions that,when executed by the processing module, causes the processing module to:interpret the plurality of scores in relation to the initialinterpretation to determine a confidence level of the initialinterpretation; and when the confidence level of the initialinterpretation compares favorably to a confidence threshold, indicatethat the initial interpretation is reliable.
 14. The computer readablememory of claim 13 further comprises: the fourth memory element furtherstores operational instructions that, when executed by the processingmodule, causes the processing module to: when the confidence level ofthe initial interpretation compares favorably to the confidencethreshold, facilitate one or more of: storing a representation of theinitial interpretation in a knowledge database as curated knowledge; andstoring at least some of the plurality of entigen groups in theknowledge database as further curated knowledge; and when the confidencelevel of the initial interpretation compares unfavorably to theconfidence threshold, facilitate one or more of: generating an updatedplurality of entigen groups from an updated plurality of phrases,wherein the updated plurality of entigen groups represents a pluralityof most likely meanings for the updated plurality of phrases, whereinthe updated plurality of phrases is of the related topic; determining anupdated initial interpretation of the related topic based on theplurality of most likely meanings for the updated plurality of phrases;generating an updated plurality of scores for the updated plurality ofentigen groups based on the updated initial interpretation of therelated topic and updated source information of the updated plurality ofphrases, wherein a first score of the updated plurality of scores is fora first entigen group of the updated plurality of entigen groups;interpreting the updated plurality of scores in relation to the updatedinitial interpretation to determine an updated confidence level of theupdated initial interpretation; and when the updated confidence level ofthe updated initial interpretation compares favorably to the confidencethreshold, indicating that the updated initial interpretation isreliable.
 15. The computer readable memory of claim 13, wherein theprocessing module functions to generate the plurality of entigen groupsfrom the plurality of phrases by: determining a set of identigens foreach word of at least some words of a string of words of a first phraseof the plurality of phrases to produce a plurality of sets ofidentigens, wherein each identigen of the set of identigens is adifferent meaning of a corresponding word; and interpreting, based on aknowledge database, the plurality of sets of identigens to produce thefirst entigen group, wherein each entigen of the first entigen groupcorresponds to a selected identigen of one of the plurality of sets ofidentigens that represents a most likely meaning of a corresponding wordof the at least some of the words of the string of words, wherein thefirst entigen group is a most likely meaning of the string of words,wherein the knowledge database includes a plurality of records that linkwords having a connected meaning.
 16. The computer readable memory ofclaim 13, wherein the processing module functions to determine theinitial interpretation of the related topic based on the plurality ofmost likely meanings for the plurality of phrases by at least one of:identifying a most frequent most likely meaning of the plurality of mostlikely meanings for the plurality of phrases as the initialinterpretation; identifying an entigen group associated with the mostfrequent most likely meaning of the plurality of most likely meaningsfor the plurality of phrases as the initial interpretation; andidentifying an entigen group associated with a most likely meaning thatcompares favorably to a search phrase as the initial interpretation. 17.The computer readable memory of claim 13, wherein the processing modulefunctions to generate the plurality of scores for the plurality ofentigen groups based on the initial interpretation of the related topicand source information of the plurality of phrases by one or more of:determining a reliability score for the first entigen group based on areliability level of a first source associated with a first phrase thatis utilized to generate the first entigen group; determining an agingscore for the first entigen group based on an age of the first phrase;determining an alignment score for the first entigen group based onalignment with the initial interpretation, wherein an alignment scorefor a confirming alignment is greater than an alignment score for adisconfirming alignment; and determining the first score for the firstentigen group based on a weighting approach and the reliability scorefor the first entigen group, the aging score for the first entigengroup, and the alignment score for the first entigen group.
 18. Thecomputer readable memory of claim 13, wherein the processing modulefunctions to interpret the plurality of scores in relation to theinitial interpretation to determine the confidence level of the initialinterpretation by: identifying confirming entigen groups of theplurality of entigen groups favorably aligned with the initialinterpretation; identifying disconfirming entigen groups of theplurality of entigen groups unfavorably aligned with the initialinterpretation; and determining the confidence level based on aweighting approach and scores for the confirming entigen groups andother scores for the disconfirming entigen groups.